Improving text summarization of online hotel reviews with review helpfulness and sentiment

Tourism Management 80 (2020) 104122

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Improving text summarization of online hotel reviews with review helpfulness and sentiment Chih-Fong Tsai a, Kuanchin Chen b, Ya-Han Hu a, c, d, *, Wei-Kai Chen a, e a

Department of Information Management, National Central University, Taiwan Department of Business Information Systems, Western Michigan University, Kalamazoo, MI, USA c Center for Innovative Research on Aging Society, National Chung Cheng University, Taiwan d MOST AI Biomedical Research Center at National Cheng Kung University, Taiwan e Department of Information Management, National Chung Cheng University, Taiwan b

A R T I C L E I N F O

A B S T R A C T

Keywords: Online hotel reviews Text summarization Helpful review selection Sentiment analysis TripAdvisor

The considerable volume of online reviews for today’s hotels are is difficult for review readers to manually process. Automatic review summarizations are a promising direction for improving information processing of travelers. Studies have focused on extracting relevant text features or performing sentiment analysis to compile review summaries. However, numerous reviews contain nonspecific or nonsentimental content, hindering the ability of sentiment-based techniques’ to accurately summarize useful information from hotel reviews. This paper proposes a systematic approach that first constructs classifiers to identify helpful reviews and then classifies the sentences in the helpful reviews into six hotel features. Finally, the sentiment polarities of sentences are analyzed to generate the review summaries. Experiment results indicated that the performance of the proposed approach was superior to other methods.

1. Introduction The Internet has become integral to travel, providing a variety tools and resources; a multitude of sources are available online, and these include blogs, electronic forums, virtual communities, and online newsgroups, that enhance every stage of travel from pretravel planning to reviewing activities online (Palakvangsa-Na-Ayudhya, Sriarunrun­ greung, Thongprasan, & Porcharoen, 2011; Marrese-Taylor, Vel� asquez, & Bravo-Marquez, 2014; Hu, Chen, & Lee, 2017a; Hu, Zhang, Gao, & Bose, 2019). Online reviews help shape the travel experience of other consumers, thus representing a powerful form of word-of-mouth called electronic word-of-mouth (eWoM). TrustYou.com reported that 95% of customers browse online hotel reviews before booking hotels (Ady & Quadri-Felitti, 2015). Studies have also confirmed the effect of online reviews on consumers and the hotel industry as a whole (Ghose & Ipeirotis, 2011; Lee, Law, & Murphy, 2011; Yang, Myung, & Lee, 2009). TripAdvisor (TripAdvisor.com) and Yelp (Yelp.com) are among the popular platforms for leaving feedback and ratings and sorting online reviews. However, these platforms still require that consumers read through a large number of reviews to form personal opinions on the facilities of interest. As a result, information overload or early

abandonment of research into online reviews is probable (Li, Xu, Tang, Wang, & Li, 2018). Furthermore, review quality is not always consistent. Some reviews contain irrelevant or biased information, whereas other reviews provide objective evaluations and helpful information. There­ fore, consumers need to search through a large number of reviews and devote sufficient mental energy to separate objective, high-quality re­ views from low-quality or biased reviews. Additionally, users must generally browse a sufficient number of reviews to obtain the desired information to make decisions. Performing such extensive research can be taxing on a consumer’s time and energy, and thus, it would be beneficial for them to have an efficient means of processing the considerable volume of online reviews. One effective approach for overcoming information overload and a lack of efficient resources for review analysis is to generate a summary from online reviews (Abuobieda, Salim, Albaham, Osman, & Kumar, 2012; Amplayo & Song, 2017; Atkinson & Munoz, 2013; Ding, Liu, & Yu, 2008; Hu, Chen, & Chou, 2017b; Marrese-Taylor et al., 2014; Mason, Gaska, & Van Durme, 2016; Popescu & Etzioni, 2005; Tan, Kotov, Mohammadiani, & Huo, 2017; Zhan, Loh, & Liu, 2009). Specifically, for a set of online reviews, sentences that provide valuable information can be extracted to form a summary report. A summarized review can offer

* Corresponding author. Department of Information Management, National Central University, Taiwan. E-mail address: [email protected] (Y.-H. Hu). https://doi.org/10.1016/j.tourman.2020.104122 Received 17 February 2019; Received in revised form 22 March 2020; Accepted 24 March 2020 Available online 4 April 2020 0261-5177/© 2020 Elsevier Ltd. All rights reserved.

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than conventional approaches do (i.e., those that do not consider hotel features)?

readers relevant information in a manner that is less demanding on the user. A survey conducted by TrustYou.com in 20151 revealed that 52% of customers considered systems that provide relevant summarizations of online hotel reviews to be more user-friendly than systems that do not. Review summarization is useful to consumers for any hotel with a sizable number of online reviews. Although studies have proposed various methods for hotel review summarization, two major shortcom­ ings need to be considered (Hu et al., 2017a; Marrese-Taylor et al., 2014; Tan et al., 2017). The first problem is devising an approach to properly retain useful hotel reviews for summarization. Studies have demonstrated that identifying helpful reviews is crucial to ease information overload (Cao, Duan, & Gan, 2011; Chua & Banerjee, 2015; Ghose & Ipeirotis, 2011; Huang, Chen, Yen, & Tran, 2015; Hwang, Lai, Chang, Jiang, & Chang, 2015; Mudambi & Schuff, 2010). Most modern travel websites allow readers to vote on the helpfulness of online reviews. A helpful review is one that receives enough votes; however, this number alone can be biased. For example, votes can be significantly affected by the length of time a review has been posted (Hu & Chen, 2016; Hu et al., 2017a). Newly posted reviews are unlikely to receive more votes on average than older reviews because time is also a factor for the accumulation of votes on helpfulness. Therefore, if the number of votes received by a review is the criterion for selecting reviews for summarization, newer reviews are at a disadvantage. However, newer reviews provide more recent hotel assessments, thus likely offering more useful and relevant information to consumers. Because the factors affecting online review helpfulness have been studied extensively in the literature, developing a prediction model for identifying useful hotel reviews before performing review summa­ rization is feasible. The second problem is extracting a summary that contains compre­ hensive coverage of all key aspects of hotels (also known as hotel fea­ tures). For instance, some helpful reviews cover some aspects, such as great sleep quality and friendly room service, but not other aspects, such as location, traffic, and other hotel services. Only a small number of people provide comprehensive coverage in their reviews. Summariza­ tion approaches have been proposed that may be useful in generating a summary from each review, but they cannot assemble individual aspects into comprehensive holistic coverage. Therefore, an effective text sum­ marization method must systematically select helpful hotel reviews to construct a comprehensive and holistic view of hotel experiences. Without systematic selection, the quality of summarization is limited. Therefore, in this study, we introduce a novel text summarization approach for online hotel reviews that solves these problems. Six hotel review features, namely location, sleep quality, rooms, service, value, and cleanliness, used by TripAdvisor were used to guide our hotel re­ view summarization approach. Our method was composed of three steps. The first step was constructing a classifier to select helpful and useful reviews. The second step was to categorize the sentences in the reviews according to the six hotel features. Finally, summarizations of the hotel reviews were generated based on the classified sentences, their sentiments, and their importance. This study, to the best of our knowl­ edge, is the first to consider both hotel review helpfulness and hotel features when performing a summarization. To improve the overall understanding of the quality of hotel review summarizations, this study addressed the following research questions:

The remainder of this paper is organized as follows. Section 2 pre­ sents an overview of related topics, including text summarization, sentiment analysis, factors of review helpfulness, and relevant works that have used the text summarization technique for online reviews. Section 3 describes the proposed approach. Section 4 presents the experimental setup and results. Finally, the conclusion is presented in Section 5. 2. Related work 2.1. Text summarization Text summarization or automatic text summarization is defined as the process of generating summaries from a given set of documents. The summaries should precisely describe the key content of the original documents (Gambhir & Gupta, 2017; Mani & Maybury, 1999). Gener­ ally, text summarization techniques can be divided into extraction-based and abstraction-based summarization. In extraction-based summariza­ tion, a set of important sentences is selected to form a text summary of documents; the selected sentences remain in their original form without any modification. Conversely, abstraction-based summarization applies natural language processing (NLP) techniques to interpret the infor­ mation in the original text and generate a succinct summary of the in­ formation. Creating an abstraction-based summarization is much more complicated, and thus, the majority of existing summarization systems are extraction based. In general, the operation of extraction-based summarization systems consists of three independent steps (1) creating an intermediate repre­ sentation (i.e., set of features): of the input that captures the key aspects of the text, (2) scoring sentences based on that representation, and (3) composing a summary from the selected sentences (Allahyari et al., 2017; Gupta & Lehal, 2010; Nenkova & McKeown, 2012). In recent years, extraction-based summarization techniques have been adopted in various domains (Hu et al., 2017b; Lloret et al., 2015). Extraction from multiple online reviews requires the use of a multi­ document summarization technique. Reviews written by different on­ line users on the same topic usually contain various opinions and perspectives. Therefore, multidocument extraction-based summariza­ tion of online reviews is a more complex and challenging task than single document extraction. 2.2. Online review summarization Online communities have facilitated the expression of opinions and reviews of product. Online reviews are a form of eWOM that can considerably affect a business’ image and the profitability of products (Chen & Zimbra, 2010; Shi & Liao, 2017). Therefore, online reviews enable companies to modify their services, product offerings, and other activities associated with customer encounters. However, properly distilling information from the tremendous volume of online reviews requires time, cost, and effort. Table 1 summarizes studies on online review summarizations. The data sources, use of sentence and document features, consideration of sentiment analysis, and evaluation of review helpfulness are compared. Some studies have focused solely on sentence and document fea­ tures, such as sentence length, term frequency, and order of sentences, for online review summarizations. For instance, Zhan et al. (2009) proposed an automatic summarization approach based on the analysis of the internal topic structure of reviews to collect customer concerns. They adopted a sentence ranking and clustering approach to discover and extract salient topics to produce a summary of ranked topics. Their experiment results based on Amazon product reviews exhibited superior summarization performance and user satisfaction to the approaches of

● Can the consideration of review helpfulness improve the results of hotel review summarization? ● Does the proposed review summarization approach, in which six hotel features are considered, generate higher-quality summaries

1 http://www.trustyou.com/press/study-reveals-travelers-prefer-summ arized-review-content-full-text-reviews.

2

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product features, each accompanied by a list of associated opinions ranked based on strength. This output information can then be used to generate various types of opinion summaries. Ding et al. (2008) proposed a holistic lexicon-based approach that extracts opinions by exploiting external evidence and linguistic con­ ventions of natural language expression. This approach can handle context-dependent opinion words and language constructs that are im­ pactful, based on linguistic patterns. Their approach significantly out­ performed existing state-of-the-art methods. Amplayo and Song. (2017) presented a fine-grained sentiment extraction model for short texts with two components: sentiment clas­ sification and aspect extraction. The sentiment classifier uses a three-level classification approach. The first-level classifiers employ NLP techniques, which are lexical and syntactical. The second-level classi­ fiers are based on the support vector machine (SVM) classifiers that handle different n-gram feature vectors from different dictionaries. The third level combines the first two levels using a simple feedforward neural network. The aspect extractor extends the biterm topic model using an extension of the latent Dirichlet allocation topic model for short texts. This model outperformed baseline models and industry-standard classifiers, whereas the aspect extractor outperformed other topic models in terms of aspect diversity and aspect-extracting power. Hu et al. (2017b) proposed a novel multitext summarization tech­ nique to identify the top-k most informative sentences of TripAdvisor reviews. A new sentence value metric was developed, with content and sentiment emphasized as the crucial aspects of similarity between two sentences. The top-k sentences are identified by the k-medoids clustering algorithm to partition sentences into k groups, where the medoids from these groups are selected as the final summarization results. This approach outperformed two conventional approaches, and, of the 20 subjects investigated, the majority believed that the proposed approach could provide more comprehensive hotel information. Tan et al. (2017) proposed the two-step extractive summarization method, named topic anchoring-based review summarization (TARS). The first step of TARS uses the topic aspect sentiment model (TASM) to identify aspects of sentiment-specific topics in a collection of reviews. TARS is then used on the output from the TASM to rank review sentences based on word importance. TARS was tested on TripAdvisor reviews with both quantitative and qualitative measures. The results were promising. Abdi et al. (2018) developed a machine learning–based approach to summarize user opinions expressed in reviews by calculating a sentence sentiment score for sentence-level classification, extracting a vector representation for each word, and determining salient sentences based on statistical and linguistic knowledge. The results demonstrated that integrating the SVM classifier and information gain for feature selection significantly improved performance. Ma and Li (2019) developed a sentiment-preserving document summarization technique that focused on summarizing a long document and preserving both readability and sentiments. An end-to-end weakly supervised extractive framework, consisting of a hierarchical document encoder, sentence extractor, sentiment classifier, and discriminator, was used to categorize the extracted summaries. The experiment results demonstrated that the proposed framework generated reasonable sum­ maries with user-defined compression ratios. Although sentence or document features can also be analyzed to create quality review summaries, Table 1 illustrates that the majority of studies have focused on building review summarizations from review sentiments. However, review helpfulness has not been considered. If review helpfulness is not considered, review summarization is per­ formed on all reviews without discrimination, and even poor-quality reviews are taken into account. The poorer reviews are included, the more probable it is that the summarization will be biased toward these poor reviews, thereby degrading the quality of review summarizations. However, our proposed approach is based on first selecting helpful re­ views and then classifying the sentences describing hotel features.

Table 1 Comparison of relevant studies on review summarization. Studies

Datasets

Hu and Liu (2004) Popescu and Etzioni (2005) Ding et al. (2008) Zhan et al. (2009) Ganesan, Zhai, and Han (2010)

Amazon

✓

Amazon

✓

Wang, Zhu, and Li (2013) Marrese-Taylor et al. (2014) Lloret et al. (2015) Amplayo and Song. (2017) Hu et al. (2017b) Tan et al. (2017) Abdi, Shamsuddin, Hasan, and Piran (2018) Ma and Li (2019) This study

Amazon Amazon TripAdvisor, Amazon, Edmund Amazon

Sentence/ Document features

IMDB, Yelp TripAdvisor

Helpfulness of reviews

✓ ✓ ✓

✓

TripAdvisor Amazon, WhatCar, Twitter Rotten Tomatoes, Amazon, Naver movie TripAdvisor TripAdvisor DUC 2002, Movie reviews

Sentiment analysis

✓ ✓

✓

✓ ✓ ✓

✓

✓ ✓

✓

opinion mining and clustering summarization. Ganesan et al. (2010) proposed a novel graph-based summarization framework to produce concise summaries from reviews with highly redundant opinions. Particularly, a textual graph to represent the text to be summarized was constructed. Then, three unique properties of this graph were used to explore and score various subpaths to generate po­ tential abstractive summaries that omit highly redundant discussions and identify existing sentence structures and collapsible structures. The proposed approach was closer to human performance than the baseline method was. Wang et al. (2013) developed a web-based review summarization system called SumView in which the most representative expressions and customer opinions in the reviews of various product features were automatically extracted. Specifically, users can enter their desired product features into SumView; then, a feature-based weighted nonnegative matrix factorization algorithm is performed for the selec­ tion of sentences to form a summary for each product feature. The proposed approach was assessed using Recall-Oriented Understudy for Gisting Evaluation and a user study, and the experiment results based on DUC 2005 and 2006 datasets demonstrated that the proposed approach outperformed several traditional summarization methods. Lloret et al. (2015) proposed a novel concept-level approach for ultraconcise opinion abstractive summarization. The three main steps of the proposed algorithm were syntactic sentence simplification, sentence regeneration, and internal concept representation. Datasets were collected from Amazon, WhatCar, and Twitter, and experiment results indicated that the proposed approach outperformed several state-of-the-art methods and was more robust for analyzing noisy data when sentence regeneration was added. Other than considering sentence or document features, numerous studies have utilized sentiment analysis techniques to generate review summarizations. Hu and Liu (2004) developed a feature-based sum­ marization system to mine product features and opinion polarity; the system yields a feature-based summary of a large number of product reviews. Similarly, Popescu and Etzioni (2005) introduced the unsu­ pervised information extraction system OPINE, which outputs a set of 3

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Finally, sentiment analysis is performed to produce the summaries of hotel reviews. This provides a methodological improvement that sepa­ rates high-quality reviews from poor-quality reviews, which eventually improves the summarization quality.

and unhelpful reviews. To analyze the helpfulness of the reviews, four supervised learning algorithms were used to individually construct four classifiers for comparison, namely decision tree (DT), random forest (RF), logistic regression (LR), and SVM (Lee, Hu, & Lu, 2018). Furthermore, a 10-fold cross-validation strategy was applied to train and test the classifiers.

3. Proposed approach

3.2.1. Review quality The study by Forman, Ghose, and Wiesenfeld (2008) was one of the first to establish a link between review readability and helpfulness, indicating that high readability has a positive effect on the helpfulness of reviews and spelling errors have a negative effect. Ghose and Ipeirotis (2011) also indicated that reviews with high readability are likely to be labeled helpful reviews. In the present study, readability features related to the length of a review were first considered, including the review rating, number of characters, number of syllables, number of words, number of sentences, average number of syllables, and average number of words (O’Mahony & Smyth, 2010; Ghose & Ipeirotis, 2011). Gener­ ally, review ratings range from 1 to 5, with 1 indicating that the reviewer has a highly negative opinion and 5 indicating that they have a highly positive opinion. Furthermore, numerous indices have been proposed for measuring readability in the literature, but no index is indisputably optimal (Hu & Chen, 2016; Lee et al., 2018; Martin & Pu, 2014). Therefore, the following six common indices were used to represent review readability:

Fig. 1 illustrates the architecture of the proposed approach that consists of four steps. The first step involved data collection and pre­ processing, including spell checking and stemming. The second step is the selection by trained classifiers of helpful reviews from the dataset. The third step involves review sentence categorization based on hotel feature extraction and classification. The final step produces summaries of hotel reviews by processing sentence polarity and value. The following subsections describe these four steps in detail. 3.1. Dataset collection and preprocessing Hotel reviews were collected from TripAdvisor.com, which is one of the most popular platforms for travelers to share their tourism and hospitality experiences. Relevant information from the hotel reviews included the ratings, review title and content, reviewers’ basic infor­ mation, numbers of feedback votes regarding review helpfulness, and the date of review publication. Two data preprocessing tasks were executed after data collection: correction of spelling errors with Google Spell Check2 and text pre­ processing, including word segmentation, stemming, and part-of-speech tagging, using Stanford CoreNLP3 (Manning et al., 2014).

● Flesch Reading Ease Scale (FRES) (Kincaid, Aagard, O’Hara, & Cot­ trell, 1981) � � � � Wordi Syllablesi 84:6 (2) FRESi ¼ 206:835 1:015 Sentencei Wordi

3.2. Review helpfulness analysis As was stated in Section 1, recent hotel reviews usually reflect the current facilities and services better and more accurately than older reviews, but more recent reviews have not had the time to accumulate votes on review helpfulness. Generally, the longer a review has been published, the more likely it is to receive more votes. The length of time that a review has been posted has been demonstrated to affect the review helpfulness rating (Hu et al., 2017a; Liu, Huang, An, & Yu, 2008; Zhu, Yin, & He, 2014). Therefore, measuring review helpfulness using the number of votes causes systematic bias. To reduce the effect of longevity bias, review helpfulness for the ith review is defined as follows: Helpfulnessi ¼

Helpful ​ Votesi Elapsed ​ Dayi

● Automated Readability Index (ARI) (Smith & Kincaid, 1970) � � � � Charactersi Wordi ARIi ¼ 4:71 þ 0:5 21:43 Wordi Sentencei ● Flesch-Kincaid Grade Level (FGL) (Kincaid et al., 1981) � � � � Wordi Syllablesi FGLi ¼ 0:39 þ 11:8 15:59 Sentencei Wordi

(1)

● Gunning fog index (FOG) (Gunning, 1969) �� � � �� Wordi Complexi FOGi ¼ þ 100 Sentencei Wordi

where Helpful Votesi is the number of votes received for review help­ fulness of the ith review, and Elapsed Dayi is the period between the review publication date and the date of data collection. The approach suggested by Martin and Pu (2014) was employed to determine helpful and unhelpful reviews to later train the classifiers. This approach considers the top 1% of reviews, ranked by review helpfulness according to Equation (1), as helpful reviews, with the remaining 99% of reviews being regarded as unhelpful reviews. Studies have provided comprehensive analyses of predictors for re­ view helpfulness (Cao et al., 2011; Ghose & Ipeirotis, 2011; Mudambi & Schuff, 2010). Table 2 summarizes three key categories of predictors that are common in these studies: review quality (including rating, length, and readability of a review), reputation of reviewers (including their basic characteristics and historical records), and review sentiments (including their subjective qualities and sentiment polarities). A total of 28 variables were used in this study as the input features to train the classifiers on a dichotomous output variable with two classes: helpful 2 3

(4)

(5)

● Coleman-Liau Index (CLI) (Coleman & Liau, 1975) CLIi ¼ 0:0588L

0:296S

15:8

(6)

where L is the number of letters for every 100 words and S is the number of sentences for every 100 words. ● Simple Measure of Gobbledygook (SMOG) (McLaughlin, 1969) rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 30 SMOGi ¼ 1:0430 Complexi � þ 3:1291 (7) Sentencei

For the aforementioned indexes, Wordi , Sentencei , Syllablesi , Charactersi , and Complexi , which represent the number of words, sen

https://code.google.com/p/google-api-spelling-java/. http://stanfordnlp.github.io/CoreNLP/. 4

­

(3)

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Fig. 1. Architecture of the proposed approach.

Table 2 Variables used for review helpfulness prediction. Category

Variables

Types

Definition

Reference

Review Quality

Rating Characters Syllables Word Sentence Avg Syllables Avg Word FRES ARI

Cat. Num. Num. Num. Num. Num. Num. Num. Num.

1 to 5 The numbers of characters The numbers of syllables The numbers of words The number of sentences ‘Syllables’ divides ‘Sentence’ ‘Word’ divides ‘Sentence’ Flesch Reading Ease Scale (0–100) Automated Readability Index

FGL FOG CLI SMOG Reviewer level Join days Num reviews Num Hotel Num Votes Avg Votes Dev Votes Avg Rating Dev Rating Strong positive Positive Neutral Strong negative Negative Sentiment class

Num. Num. Num. Num. Cat. Num. Num. Num. Num. Num. Num. Num. Num. Num. Num. Num. Num. Num. Cat.

Flesch-Kincaid Grade Level Gunning Fog Index Coleman-Liau Index Simple Measure of Gobbledygook NewReviewer/Reviewer/SeniorReviewer/Contributor/SeniorContributor/TopContributor The numbers of days by the data collection date minus the registered date The numbers of published reviews The numbers of hotels in the published reviews The numbers of votes for the helpful feedbacks ‘Num Votes’ divided by ‘Num Reviews’ Standard deviation of ‘Avg Votes’ 1 to 5 Standard deviation of ‘Avg Rating’ Strong positive score Positive score Neutral Strong negative score Negative score Negative/neutral/positive

O’Mahony and Smyth (2010) Ghose and Ipeirotis (2011) Ghose and Ipeirotis (2011) O’Mahony and Smyth (2010) Ghose and Ipeirotis (2011) O’Mahony and Smyth (2010) Kincaid et al. (1981) Ghose and Ipeirotis (2011) Ghose and Ipeirotis (2011) Liu and Park (2015) Ghose and Ipeirotis (2011) Ghose and Ipeirotis (2011) Ghose and Ipeirotis (2011) Ghose and Ipeirotis (2011) O’Mahony and Smyth (2010) Lee et al. (2018) Ghose and Ipeirotis (2011) This study Ghose and Ipeirotis (2011) Ghose and Ipeirotis (2011) This study Ghose and Ipeirotis (2011) This study Hu, Bose, Koh, and Liu (2012) Hu et al. (2012) Hu et al. (2012) Hu et al. (2012) Hu et al. (2012) Hu et al. (2012)

Reviewers

Review Sentiments

Cat.: categorical, Num.: numerical.

tences, syllables, characters, and complex words in the ith review, respectively; a complex word is defined as one having three or more syllables. FRESi produces a score regarding reading ease that ranges from 0 to 100, where 90–100, 80–89, 70–79, 60–69, 50–59, 30–49, and 0–29 represent readability levels of Very Easy (approximately 5th-grade reading level), Easy (approximately 6th-grade reading level), Fairly Easy (approxi­ mately 7th-grade level), Standard (approximately 8th-grade to 9th-grade

level), Fairly Difficult (approximately 10th-grade to 12th-grade level), Difficult (approximately college graduate level), and Very Confusing, respectively. The output values of ARIi , FGLi , FOGi , CLIi , and SMOGi indicate grade levels in the United States. 3.2.2. Reviewers Reviewer characteristics have also been used to assess the helpful­ ness of reviews. Forman et al. (2008) reported that when a review 5

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contains the reviewer’s identity or basic characteristics, customers are more likely to rate the review as helpful. Hu, Liu, and Zhang (2008) further demonstrated that customers not only care about review ratings but also reviewer reputations and the number of reviews that reviewers have provided previously. Similarly, Otterbacher (2009) operational­ ized reviewer reputation using reviews at Amazon.com. Other reviewer traits are also of interest, including reviewer grade (or reviewer level), date of registration, and previous reviews. Reviews with earlier publication dates are likely to receive more helpfulness endorsements than later reviews. Similarly, reviewers who have been registered longer have had more time to publish reviews than those who registered recently. Additionally, their reviews may have more time to receive users’ attention. Therefore, we considered a total of nine vari­ ables as the input features with respect to reviewers (see Table 2).

nouns were determined using the rankings of the term frequencies. Finally, a set of hotel features was obtained according to the highest-ranked nouns. jRj � X � jRj � tfidf tj ¼ tf ri ; tj � log df tj i¼1

(8)

where R is the set of helpful reviews, tfðri ; tj Þ is the frequency of the term tj in the ith helpful review (i.e., ri ), and dfðtj Þ is the number of helpful reviews where tj appears in R. Subsequently, representative nouns were determined using the rankings of the term scores. Finally, a set of hotel features were obtained according to the highest-ranked nouns. The terms that were relevant to each of the six hotel features were collected manually, and six hotel feature dictionaries were constructed for review sentence categoriza­ tion. Table 3 illustrates the example index terms for the six hotel fea­ tures; the total number of index terms was 750. Once the six hotel feature dictionaries were established, each review sentence was categorized by matching index terms in the dictionaries. If all matched index terms in a review sentence belonged to one of the dictionaries, this review sentence was categorized into that hotel feature directly. If the matched index terms in a review belonged to more than two hotel features, the system counted the number of matched index terms for each of the six hotel features and the review sentence was categorized to the hotel feature that had the majority count. If a review sentence had equal numbers of index terms on two hotel features, that review sentence was categorized under both hotel features. When a term was not identified in any of the feature dictionaries, the term’s co-occurrence between the six hotel feature dictionaries was calculated based on Pointwise Mutual Information (PMI; Manning & Schütze, 1999):

3.2.3. Review sentiments Sentiment analysis is also called opinion mining; it is a technique to analyze sentiment polarity from texts. When used in analyzing user re­ views, it can uncover users’ experiences with products. Sentiments are generally in one of three forms: positive, negative, or neutral (Ding et al., 2008; Hu, Koh, & Reddy, 2014; Liu, 2012; Marrese-Taylor et al., 2014; O’Mahony & Smyth, 2010; Zhan et al., 2009). Feature extraction and opinion orientation identification are two major necessary steps in sentiment analysis (Bai, Padman, & Airoldi, 2005; Hu & Liu, 2004; Liu, 2012; Lloret et al., 2015; Marrese-Taylor et al., 2014; Palakvangsa-­ Na-Ayudhya et al., 2011; Popescu & Etzioni, 2005; Shah et al., 2016; Turney, 2002; Zhan et al., 2009). Regarding feature extraction, NLP techniques are used to automatically identify relevant terms in user opinions. After feature extraction, opinion orientation identification is performed to determine the sentiment polarity of customers’ opinions. Studies have demonstrated that review helpfulness is heavily dependent on sentimental terms (Mudambi & Schuff, 2010). Ghose and Ipeirotis (2011) studied product reviews from Amazon.com and deter­ mined that review subjectivity and readability significantly affect re­ view helpfulness ratings; specifically, reviews containing both subjective and objective sentences are more helpful for customers than reviews that do not contain both. According to Korfiatis, García-Bar­ �nchez-Alonso (2012), the presence of semantic elements, iocanal, and Sa such as sentimental terms, is more influential than review length. Hu et al. (2014) used 4405 book reviews from Amazon.com to examine the relationship between ratings of reviews, review sentiments, and sales. The results revealed that the sentimental features of the reviews were the principal factor affecting sales. The Stanford CoreNLP toolkit (Manning et al., 2014) was used in this present study to analyze review sentiment because it is widely adopted in the research community and displays favorable generalizability across various application domains. The Stanford CoreNLP toolkit can identify the sentiment polarity of a sentence by using a supervised learning-based method to assess the relationships between terms and predictors. Stanford CoreNLP supports five categories of sentiment, namely strong positive, positive, neutral, strong negative, and negative.

� PMI ti ; tj ¼

countðt1 þ t2 Þ countðt1 Þ*countðt2 Þ

(9)

where count(⋅) is the number of appearances in Google, ti is the term being compared, and tj is the term in the feature dictionary. Therefore, ti belongs to a specific feature dictionary if it has the highest co-occurrence with a term in the feature dictionary. 3.4. Review summarization The final step was to generate a review summary from six sets of hotel feature sentences. To obtain objective opinions, we first parti­ tioned each set of sentences into two subsets by their sentiment polarity. After that, the importance score for each sentence was calculated ac­ cording to the sentence content. Because the content of some sentences may be highly similar, selecting sentences with high importance scores may generate a set of sentences having similar meanings, resulting in a lost opportunity to collect useful information from other sentences. Therefore, for each sentence set with a specific sentiment polarity, a Table 3 Six example sets of index terms for the six hotel features.

3.3. Review sentence categorization TripAdvisor.com has six different hotel aspects, which are location, sleep quality, rooms, service, value, and cleanliness. After identifying helpful hotel reviews, each sentence was categorized into predefined hotel features. Before sentence categorization, the set of index terms for each hotel feature was determined. This step consisted of extracting relevant terms for hotel features from helpful reviews. Zhan et al. (2009) proposed an effective approach to discover all topics and their repre­ sentative index terms for sentence categorization; the results indicated that the proposed method outperformed conventional approaches. Therefore, the approach of Zhan et al. (2009) was employed to extract the nouns of each review sentence, with each term frequency being measured as defined in Equation (8). Subsequently, representative

Hotel feature

Index terms

Location

airport, area, bus, city, distance, location, local, place, site, station, street, train, way air, atmosphere, bed, bedtime, door, dream, neighbor, night, noise, pillow, refreshment, sleep bathroom, bar, bedroom, capacity, facility, floor, pool, room, size, space, TV, view, window benefit, caf�e, class, drink, hospitality, member, noise, reception, service, smile, staff, Wi-Fi, wine amount, breakfast, cost, deal, expense, experience, price, quality, review, restaurant, star, value, bath, clean, cleanliness, comfort, facility, freshness, lounge, neatness, standard, toilet

Sleep quality Room Service Value Cleanliness

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sentence clustering method was employed to group sentences with high similarity; the most representative sentence in a cluster was then selected in the review summary. Specifically, the sentences in the same cluster were ranked according to their importance scores and the topranked sentences were selected. The final summary of a review was summarized by the selected sentences and the other sentences in the hotel feature sets.

The opinion lexicons (i.e., a list of English positive and negative opinion words or sentiment words) were created by Hu and Liu (2004) and comprise approximately 6800 opinion words extracted from online consumer reviews. Sentences with higher subjectivity are considered

3.4.1. Identification of sentence polarity To identify the sentiment polarity of sentences and the strengths and weaknesses of subjective expressions, OpinionFinder, which is a powerful toolkit for conducting sentiment analysis, subjectivity analysis, and negation detection, was used. OpinionFinder can identify opinions, sentiments, speculations, and other categories of thought disclosed in the text (Wilson et al., 2005a). A contextual polarity recognition approach was also used to detect negations and polarity inversion (Wilson, Wiebe, & Hoffmann, 2005b). The positive and negative senti­ ment classes were in the focus of this study and were represented by Positivesi and Negativesi , respectively. The identification of sentence po­ larity was based on � � Sentimentsi ¼ str possi � 2 þ weak possi str_negsi � 2 þ weak_negsi

3.4.3. Sentence clustering The next step entailed grouping or clustering similar sentences that describe similar hotel features from the sets of sentences with positive and negative sentiments. The similarity between two sentences si and sj can be measured as follows:

orientop

more essential than other sentences. If dðopj ;feasj Þ is higher than 1, then 1 is

Similaritysi ;sj ¼

The experimental evaluation consisted of two parts: (1) the selection of the best classifiers for identifying helpful reviews and (2) the review summarizations from a set of helpful reviews determined using the selected classifier.

quencies of positive and negative sentiment terms with weak subjec­ tivity in a sentence si , respectively (Hu et al., 2017a). In particular, if Sentimentsi is greater than zero, the sentence belongs to the positive sentiment class; by contrast, if Sentimentsi is lower than zero, the sen­ tence belongs to the negative sentiment class.

4.1. Experimental setup The data were collected from reviews of hotels in New York, Las Vegas, Orlando, Chicago, and Miami at the beginning of March 2016. These cities were the five most popular4 travel destinations of 2015 in US. Only reviews written in English published by TripAdvisor members were analyzed. Thus, 1,170,246 reviews regarding 1488 hotels from 875,229 unduplicated reviewers were available. Using Equation (1) (given in Section 3.2), we found that the training set contained extremely low numbers of helpful reviews and high numbers of unhelpful reviews, yielding a class imbalance problem. Therefore, we randomly sampled the class of unhelpful reviews to obtain a subset of equal size to the set of helpful reviews. The balanced dataset for the experiments contained 23,430 reviews, which had been pub­ lished by 23,038 unique reviewers regarding 1009 hotels (Table 4). The descriptive statistics are provided in Appendix AAppendix A.

3.4.2. Calculation of sentence score Ding et al. (2008) and Fattah and Ren (2009) have reported that a sentence score can reflect the sentence’s level of importance in a review; the score depends on the number of terms (i.e. nouns) relevant to the hotel features, length, and subjectivity of the sentence si . These three indexes are described as follows. 1. f1 : number of nouns in the hotel feature dictionaries (11)

In this study, a sentence containing more terms in the hotel feature dictionaries was considered more essential than other sentences. 2. f2 : sentence length # of words in si Scoref2 ðsi Þ ¼ # of words in longest sentence

4.2. Performances of classifiers The first experiment entailed examining the performance levels of the constructed classifiers, where the input features included review quality, reviewers, and review sentiments and the output was review helpfulness (see Section 3.2).

(12)

This index measures the number of words for each sentence in a review. The level of importance of a sentence was considered to be dependent on the number of words. 3. f3 : the subjectivity of a sentence X Scoref3 ðsi Þ ¼

opj2si

orientopj � d opj ; feasi

(14)

4. Experiments

where str_possi and str_negsi represent the frequencies of positive and negative sentimental terms with strong subjectivity in a sentence si , respectively. Similarly, weak_possi and weak_negsi represent the fre­

# of nouns in si ，nouns 2 hotel feature # of words in si

Keywords ​ in ​ si ​ \ ​ Keywords ​ in ​ sj Keywords ​ in ​ si ​ [ ​ Keywords ​ in ​ sj

where Keywords represents the number of nouns in the hotel feature dictionaries. When the similarity is higher than the predefined threshold, then the words are grouped into the same sentence set.

(10)

Scoref1 ðsi Þ ¼

i

used for the later steps.

Table 4 Number of hotels, reviewers, and reviews for different cities.

(13)

where opj is the opinion lexicon in si , orientopj is the subjectivity (strong or weak) of an opinion lexicon in si (strong subjectivity ¼ 2; weak subjectivity ¼ 1), and dðopj ; feasi Þ is the number of opinion lexicons in the hotel feature dictionaries.

City

Number of hotels

Number of reviewers

Number of reviews

New York City Las Vegas Orlando Chicago Miami Total

382 163 251 131 82 1009

7591 7147 5033 2508 874 23,038

7660 7232 5121 2538 879 23,430

Table 5 lists the performance levels of the DT, RF, LR, and SVM

4

7

http://www.tripadvisor.com/TravelersChoice.

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classifiers for the reviews of hotels in the five cities. Notably, the five classifiers were constructed using Weka data mining software (Hall et al., 2009). The RF classifier achieved the highest average performance level for accuracy, sensitivity, and area under the receiver operating characteristic curve (AUC), and the SVM classifier outperformed the others in specificity. Therefore, RF was used for the proposed approach in later experiments.

the tutorial, we provided a detailed introduction to the interfaces and functions provided on TripAdvisor. Two hotels were then preselected for each participant. The participants were required to browse four review summaries generated by the four summarization methods for each hotel; the sequence of presenting review summaries was randomized using Latin square design. Using the hyperlinks at the end of the tutorial in Google Forms, each participant could explore the preassigned hotel re­ view summaries using the developed web interface (i.e., blind review of the summarization results). Finally, participants ranked review sum­ marization results for each hotel based on their quality. Example sum­ marization results for Wyndham Garden Hotel are provided in Appendix B. Table 6 presents the ranks of the summarization results of the four methods by the users. The number indicates the number of users to assign that ranking, and the percentage in parentheses indicates the proportion of users out of the 30 users investigated. As illustrated by these results, despite user evaluation usually being subjective, the proposed approach (i.e., method D) obtained the most votes for ranking 1 and the least votes for ranking 4. Furthermore, ANOVA was performed to examine the significance of the differences between the four methods (Table 7). The analysis results revealed that the proposed approach demon­ strated significantly superior performance to the other three methods (p < 0.05), especially methods A and B (p < 0.001). This may be because the process of helpful review selection was not performed in methods A and B, which may have produced summarization results that contained some summaries from unrepresentative sentences. This was confirmed by the result of C þ D (i.e., the two methods with helpful review se­ lection) versus A þ B (i.e., the two methods without helpful review se­ lection), where helpful review selection exhibited significant improvement over the methods that did not select helpful reviews. Considering hotel feature classification can also provide a significant performance improvement from methods that do not consider hotel feature classification (i.e., B þ D vs. A þ C).

4.3. Summarization results To assess the summarization results, four different approaches, namely methods A–D, were compared. Methods A and B produced re­ view summaries according to the whole review set, whereas methods C and D applied the RF classifier to select helpful reviews and subse­ quently produce the review summaries from the helpful review set. (A) Without helpful review selection and hotel feature classification: This method does not perform the process of selecting helpful reviews or the hotel feature classification to produce the sum­ maries of reviews. (B) Without helpful review selection: This method does not perform the process of selecting helpful reviews, but the process for hotel feature classification is performed. (C) With helpful review selection and without hotel feature classifi­ cation: The process of selecting helpful reviews is performed, but the process of hotel feature classification is not performed. (D) With helpful review selection and hotel feature classification (i.e., the proposed approach): The selection of helpful reviews and classification of hotel features are both performed. To evaluate the summarization results of the four methods, a ques­ tionnaire website was developed to enable users to participate in the experimental evaluation. The interface demonstrating the summariza­ tion results of the four methods was written in PHP, MySQL, and JavaScript. User evaluation was a time-consuming task because asking users to assess numerous summarization results at one time is difficult. Therefore, for each of the five travel cities, we randomly selected two hotels as the experimental objects, yielding 10 total experimental ob­ jects. Thirty participants who had booked hotels on TripAdvisor were recruited for the experimental evaluations. Before the experiment, each participant was required to complete a tutorial using Google Forms. In

5. Conclusion High-quality summaries of online hotel reviews must be provided to customers who intend to make optimal travel decisions. This study presented a novel approach for improving text summarization results for online hotel reviews. In contrast to studies that used text summarization techniques for generating review summaries, the proposed approach focuses on selecting helpful reviews first and then categorizing the sentences of the selected reviews under their corresponding hotel feature classes. Finally, relevant summaries are generated from the classified sentences for specific hotel features. Our experiments were based on a chosen hotel review dataset collected from TripAdvisor.com. The first experiment involved assessing the classification performance levels of different classifiers for helpful review selection. The results indicated that the RF classifier achieved the most favorable performance, yielding more than 70% classification ac­ curacy and more than 80% AUC. This reflects the applicability of con­ structing a classifier for effectively selecting helpful reviews. The second experiment entailed comparing four different methods with and without the two processes of helpful review selection and hotel feature classification for hotel review summarization. In a user study with 30 participants, most users agreed that the proposed approach yielded significantly superior review summaries to those provided by

Table 5 Accuracy levels of DT, RF, LR, and SVM classifiers. Cities

Metrics

DT

RF

LR

SVM

New York City

Accuracy Sensitivity Specificity AUC Accuracy Sensitivity Specificity AUC Accuracy Sensitivity Specificity AUC Accuracy Sensitivity Specificity AUC Accuracy Sensitivity Specificity AUC Accuracy Sensitivity Specificity AUC

0.708 0.713 0.702 0.715 0.735 0.736 0.735 0.732 0.709 0.708 0.710 0.715 0.715 0.711 0.719 0.711 0.651 0.622 0.682 0.689 0.704 0.698 0.710 0.712

0.745 0.784 0.706 0.817 0.772 0.782 0.762 0.848 0.756 0.767 0.745 0.835 0.733 0.767 0.699 0.809 0.681 0.670 0.693 0.749 0.737 0.754 0.721 0.812

0.694 0.623 0.764 0.765 0.717 0.659 0.774 0.796 0.717 0.660 0.773 0.793 0.679 0.608 0.749 0.738 0.668 0.635 0.700 0.743 0.695 0.637 0.752 0.767

0.632 0.439 0.823 0.631 0.664 0.539 0.788 0.663 0.667 0.545 0.787 0.666 0.603 0.399 0.806 0.602 0.620 0.562 0.677 0.62 0.637 0.497 0.776 0.636

Las Vegas

Orlando

Chicago

Miami

Avg.

Table 6 Ranking results of the four methods. 1st-rank 2nd-rank 3rd-rank 4th-rank

8

A

B

C

D

8(13%) 14(23%) 14(23%) 24(40%)

9(15%) 17(28%) 18(30%) 16(27%)

14(23%) 18(30%) 17(28%) 11(18%)

29(48%) 11(18%) 11(18%) 9(15%)

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Our study also contributes to the literature in two respects. First, this study is the first to integrate review helpfulness predictions and review summarization techniques into information filtering of online hotel re­ views. Combining these two methods to develop smart tourism appli­ cations could be an attractive research topic in the tourism domain. Second, we further explore the possibility of detailed refinement of hotel review summarization, which may inspire the use of both text mining and machine learning techniques for hotel information extraction. In future studies, several limitations should be considered. First, because newer reviews are more frequently browsed, examining how reader endorsements of reviews change over a long period may be necessary. Furthermore, the review exposure and the order of website browsing may be crucial factors in selecting helpful reviews. Second, regarding text summarization, conjunctions such as “however” and “therefore” can be considered. Moreover, certain words or phrases which are frequently used on the Internet and emoticons can be used to identify the sentiment polarity of a sentence. Third, other travel plat­ forms for different countries should be studied.

Table 7 ANOVA results. Method I

Method II

p value

D

A B C Without helpful review selection (A þ B) Without hotel feature classification (A þ C)

<0.001*** <0.001*** 0.039* <0.001***

With helpful review selection (C þ D) With hotel feature classification (B þ D)

0.028*

the other methods. A number of practical implications may be derived from this study. First, the developed review summarization technique can be integrated into smart tourism systems in several manners. Instead of users con­ tending with numerous online reviews for hotels, car rentals, airlines, and other tourist-facing sites, our approach can be adopted to present the review summary at the point of sale or online encounter. This can provide an enriched offering for tourists to make travel decisions compared with the traditional star or point system common to most travel websites. Because reviews are posted daily, system developers of travel websites may consider running one or more batches of review summarizations daily. Large travel websites with large volumes of daily reviews may want to consider streaming review summaries as new re­ views come in because this approach could enhance competitiveness in a world with low switching costs and barriers, and prevent potential customers from switching to a competitor’s offering. Second, our approach provides a more flexible approach for travel websites to manage review content. The majority of current travel websites provide simple filters for users to screen reviews, such as re­ view posted date, travel type, and traveler rating; however, these filters cannot truly identify helpful reviews. Our system can provide intelligent search functions to filter and rank hotel reviews. For example, reviews can be grouped by hotel quality dimensions (such as cleanliness, traffic, service, and indoor/outdoor facilities), and an ordered list of informa­ tive sentences can be generated for each quality dimension based on sentence scores. As numerous travel sites are still providing overall aggregate reviewer ratings, our approach would enable them to provide fine-grained quality eWoM based on travelers’ requirements and pref­ erences. Our system can also be used to cluster helpful reviews by review polarity and the similarity of review content, giving users the flexibility to adjust how hotel reviews are presented on their personalized web pages.

Author contributions All authors had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: CFT and YHH. Acquisition of data: YHH and WKC. Analysis and interpretation of data: CFT, YHH and KC. Drafting of the manuscript: CFT, YHH and KC. Critical revision of the manuscript for important intellectual content: all authors. Study su­ pervision: YHH. Declaration of competing interest None. Acknowledgements This research was supported in part by the Ministry of Science and Technology of the Republic of China (grant number MOST 107-2410-H194-054-MY2) and the Center for Innovative Research on Aging Society from The Featured Areas Research Center Program within the frame­ work of the Higher Education Sprout Project by the Ministry of Educa­ tion (MOE) in Taiwan.

Appendix A

Table A1 Descriptive statistics for the balanced review dataset. Variable

New York City

Las Vegas

(n ¼ 7660)

Rating Characters Syllables Word Sentence Avg Syllables Avg Word FRES ARI FGL FOG

Orlando

(n ¼ 7232)

Chicago

(n ¼ 5121)

Miami

(n ¼ 2538)

(n ¼ 879)

Mean (count)

SD (%)

Mean (count)

SD (%)

Mean (count)

SD (%)

Mean (count)

SD (%)

Mean (count)

SD (%)

4.03 668.49 225.16 156.47 11.17 1.41 15.67 71.77 6.37 6.94 9.60

1.21 644.08 215.55 152.51 10.27 0.11 8.35 10.90 2.60 2.19 2.62

3.81 736.04 248.09 176.16 13.23 1.38 15.59 74.09 5.78 6.55 9.31

1.30 757.23 252.97 182.43 13.52 0.11 9.33 11.21 2.63 2.20 2.66

4.02 914.43 306.92 216.10 15.17 1.39 16.28 72.60 6.33 6.92 9.60

1.22 1027.99 342.43 244.16 15.26 0.10 10.70 10.94 2.63 2.23 2.68

4.10 612.26 207.44 141.98 10.58 1.43 14.65 70.61 6.18 6.92 9.47

1.17 647.51 216.98 152.72 9.62 0.12 6.54 11.07 2.48 2.09 2.47

3.84 607.37 205.30 141.30 10.43 1.42 15.13 71.59 6.15 6.86 9.39

1.30 556.85 186.24 129.69 9.30 0.11 8.29 11.16 2.60 2.23 2.61

(continued on next page)

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Table A1 (continued ) Variable

CLI SMOG Reviewer level N New Reviewer Reviewer Senior Reviewer Contributor Senior Contributor Top Contributor Num Review Num Hotel Num Votes Avg Votes Dev Votes Avg Rating Dev Rating Strong Positive Positive Neutral Strong Negative Negative Sentiment class Negative Neutral Positive

New York City

Las Vegas

Orlando

Chicago

Miami

(n ¼ 7660)

(n ¼ 7232)

(n ¼ 5121)

(n ¼ 2538)

(n ¼ 879)

Mean (count)

SD (%)

Mean (count)

SD (%)

Mean (count)

SD (%)

Mean (count)

SD (%)

Mean (count)

SD (%)

9.38 6.88

1.56 1.63

8.83 6.63

1.58 1.64

9.12 6.89

1.52 1.64

9.59 7.07

1.58 1.67

9.40 6.93

1.57 1.72

302 1931 1025 1018 1104 1249 1031 15.19 8.57 17.55 1.48 0.72 3.85 0.55 0.04 0.33 0.14 0.02 0.47

3.94 25.21 13.38 13.29 14.41 16.31 13.46 37.15 14.46 120.53 3.85 1.90 1.02 0.64 0.09 0.23 0.14 0.07 0.24

441 1894 897 866 943 1221 970 14.84 8.08 14.61 1.53 0.76 3.75 0.54 0.03 0.30 0.16 0.02 0.49

6.10 26.19 12.40 11.97 13.04 16.88 13.41 35.69 14.34 47.37 4.20 3.54 1.07 0.64 0.08 0.23 0.15 0.08 0.24

194 1327 685 678 752 844 641 14.54 7.56 16.85 1.82 0.76 3.79 0.56 0.04 0.31 0.14 0.02 0.50

3.79 25.91 13.38 13.24 14.68 16.48 12.52 31.91 12.68 66.49 3.64 1.95 1.05 0.66 0.09 0.22 0.14 0.06 0.23

82 658 320 263 335 456 424 18.34 9.89 16.38 1.24 0.66 3.88 0.56 0.05 0.35 0.14 0.02 0.44

3.23 25.93 12.61 10.36 13.20 17.97 16.71 43.93 17.09 75.30 3.91 1.28 0.99 0.64 0.11 0.24 0.14 0.06 0.24

26 199 99 99 113 174 169 19.15 11.17 25.47 1.85 0.82 3.75 0.61 0.04 0.30 0.15 0.02 0.49

2.96 22.64 11.26 11.26 12.86 19.80 19.23 33.42 17.67 135.76 13.56 2.49 1.02 0.63 0.09 0.23 0.15 0.09 0.24

4142 785 2733

54.07 10.25 35.68

4425 649 2158

61.19 8.97 29.84

3140 467 1514

61.32 9.12 29.56

1215 240 1083

47.87 9.46 42.67

504 86 289

57.34 9.78 32.88

Appendix B

Table B1 Review summarization results for Wyndham Garden Hotel (Method A). Positive

Negative

1 To recap: PROS: SERIOUSLY one of the most comfortable beds I’ve slept in for years, extremely good water pressure in the shower, exceptional front desk service, unbeatable location in Chelsea/Flatiron with easy access to subway& tons of restaurants, large bathroom relative to room size with all amenities (shampoo, conditioner, hair dryer) included, very good iron& board, FREE access to nearby Crunch Fitness. 2 To all the people around the world I would like very much to tell u that when u plan a trip to New York and u would like to remember every moment of your stay in the most happiest way that make sure to book this hotel, as I am flying quiet a lot about 1–2 times a month and started to stay at this hotel, I must tell u that the whole staff are THE MOST WONDERFUL NICE PEOPLE EVER SAW IN ANY HOTEL IN MANHATTAN, and I would very much like to tell the MANAGER that u should be proud for having Mr. Raj, and u should be very proud for having such super nice, good, good-hearted, wonderful people in your staff like’’ JARET00 and’ LIZETTE’ and MICHAEL[ houseman] by having them working in your Hotel u should know that u will become so so busy that u can NOT image … 3 We arrived the 23rd of December due to southwest screwing up our original hotel reservations … Lucky I called the hotel about a question and they only had me down for one room with 5 adults and most hotels do not allow more than 4 adults in a room and they were sold out!!! 4 Usually I can find some things that are right, I can’t here so the following are the wrongs: I had a 5th floor room facing front and there was not only the constant noise of the city, but construction going on in the area …

1 For a hotel this is totally unacceptable: The towel hook in the bathroom was brokenwhen the towel was lifted off the hook, the hook fell onto the floor: the wardrobe would not close when the hangars were used-i.e. the wardrobe was not deep enough: when the air-conditioning was used it made the room smell bad: very poor lighting: the hand towel holder was over the toilet roll, so after using the hand towel, the toilet tissue was damp. 2 There was even a nice handwritten note from Jeff the manager slipped under our door on the first night of our arrival (although the note regarding the complimentary snacks was a bit confusing b/c we weren’t sure if that was for the 1st day only and if it was for the snacks by the front desk or if we were supposed to have snacks in the room for us on that first date).

3 My stay in Wyndham was a very displeased experience due to: Very tight room space Poor hospitality Unfriendly staff No mini bar at the room Very bad air conditioning system(very hot or very cold, system problem not operation or maintenance- I’m a mechanical engineer) I didn’t get any assistance with my luggage upon arrival. 4 The only negative we could find was that the noise from outside was quite bothersome at times(early morning when cleaning and service vehicles were working) and also noise between room next door was REALLY bothersome, to the extent on one night the occupants of next door kept us awake till5.30 a.m.!!!! 5 I got in an email this message after I had luggage issue through checking it in with them: … In accepting the check the hotel shall not be liable for loss or damage of any nature whatsoever to say property either as a result of the ordinary or gross negligence of the hotel or its servants, agents or employees or water, fire, theft or any other cause … REWIND. 6 When we arrived to the room we phone to Hotwire to explain the situation because we feel cheat and the only solution was the possibility to cancel the reservation incurring in an extra-cost and the transferring the call to the management of the hotel, completely unable to help us more than sell to us 1 or 2 nights to find another hotel K Our Request: To have a charge back from Hotwire for the extra cost we paid for the reservations.

5 There was no tub, only a small corner shower with a curtain, very tiny bathroom counter to put curling iron etc. on, no room to put a second suitcase in the room so we placed it on the desk chair, no view, super small lobby (if you want to call it a lobby), ice machine is in the basement, minimal channels on TV, room was either freezing cold or super-hot when it was set to 75 the entire time. 6 Cozy Bedroom- Roomy Bathroom and Shower- Clean clean- Awesome staff- Excellent restaurant right on street level- Located close to Excellent Comedy Clubs, Restaurants, and Galleries- Could This Be a Better Place to Stay in All of Manhattan?

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Table B2 Review summarization results for Wyndham Garden Hotel (Method B). Positive

Negative

1 To all the people around the world I would like very much to tell u that when u plan a trip to New York and u would like to remember every moment of your stay in the most happiest way that make sure to book this hotel, as I am flying quiet a lot about 1–2 times a month and started to stay at this hotel, I must tell u that the whole staff are THE MOST WONDERFUL NICE PEOPLE EVER SAW IN ANY HOTEL IN MANHATTAN, and I would very much like to tell the MANAGER that u should be proud for having Mr. Raj, and u should be very proud for having such super nice, good, good-hearted, wonderful people in your staff like’’ JARET00 and’ LIZETTE’ and MICHAEL[ houseman] by having them working in your Hotel u should know that u will become so so busy that u can NOT image … 2 My fianc� e and I stayed her overnight for one night and ended up sleeping on the couch and the chair in the living area part of the room (we were in a suite) And the only reason we were in a suite was because they gave us a new room after we found pubic hairs in our first rooms bed in the sheets!!!!!!!!!! 3 When we returned to our (upgraded) room after the show, we found a bottle of Champagne and other treats along with a lovely card, signed with personal good wishes from the whole night staff: Winnie, Anastasia, Shannon, Lisa and Raj.

1 I could not be happy to be in the most important City in the world …. the experience was …. feel like I was in the wrong place, wrong room, and very poor service for the price I have paid.

2 Close to 5th Ave, Times square, Broadway, Penn Station, Madison Square Gardenhowever it is nestled away on a little side street so you are not right in the middle of the hustle and bustle of the big city. 3 There was even a nice handwritten note from Jeff the manager slipped under our door on the first night of our arrival (although the note regarding the complimentary snacks was a bit confusing b/c we weren’t sure if that was for the 1st day only and if it was for the snacks by the front desk or if we were supposed to have snacks in the room for us on that first date). 4 For a hotel this is totally unacceptable: The towel hook in the bathroom was brokenwhen the towel was lifted off the hook, the hook fell onto the floor: the wardrobe would not close when the hangars were used-i.e. the wardrobe was not deep enough: when the air-conditioning was used it made the room smell bad: very poor lighting: the hand towel holder was over the toilet roll, so after using the hand towel, the toilet tissue was damp. 5 We arrived late at night and we were disappointed to find a dirty room: the toilet seat had brown stains, there were used soaps and shampoos in the shower from the previous tenants, and we even found a pill on the floor. 6 But there was a 24 h deli around the corner (Olympia) that served a mean omelet and darn good bagels.

4 To recap: PROS: SERIOUSLY one of the most comfortable beds I’ve slept in for years, extremely good water pressure in the shower, exceptional front desk service, unbeatable location in Chelsea/Flatiron with easy access to subway& tons of restaurants, large bathroom relative to room size with all amenities (shampoo, conditioner, hair dryer) included, very good iron& board, FREE access to nearby Crunch Fitness. 5 This place is tops- great location, relaxed vibe, great shopping, eateries, very friendly staff and great value make this place a great place to stay- it beats staying in The Times Square area which is noisy overcrowded and overpriced hands down. 6 Bed was extremely comfortable with a great mattress, nice blankets and 4 perfect pillows.

Table B3 Review summarization results for Wyndham Garden Hotel (Method C). Positive

Negative

1 On check-in Courtney was most friendly and professional- for the rest of BOTH by stays the staff were unresponsive and dismissive- when reporting that I felt unsafe with a safe that was not secured to the wall as I travel with huge amounts of cash. 2 The bathroom was updated with modern fixtures, it had nice soft towels, a hairdryer, and True Blue bath amenities by Bath& Body Works (shampoo, conditioner, lotion, and mouthwash, 2 kinds of soap, shower cap and grooming kit V an impressive array for amid-range hotel). 3 Friendly and helpful staff Clean room with immediate help from housekeeping for any issue that came up (water did not drain well in shower) There is no front lobby to hang out at and we were not very happy with the restaurant next door that serves food for the hotel guests (breakfast was too slow, expensive, not very tasty and crampy service). 4 I hope everyone reads this so they know that not only are they getting a good deal on a great hotel in the ideal location in NYC, but they are also getting a great hotel with an ideal staff that will help you navigate the city so you get the most out of your trip. 5 Hotel overall was great … great value, great service, great rooms and great location.

1 The lobby is the size of a storefront, there are only eight rooms per floor, room size is cramped, light oak furnishings reminiscent of twenty-thirty years ago, carpet colors selected to hide dirt, drab wallpaper. 2 The second room was appropriate for the price and was consistent to the advertising but the first was clearly one of the worst rooms we have ever had anywhere K dark oppressive view. 3 It’s small, the elevators run slow(mostly due to the fact that the housekeeping staff only has access to 7 rooms on each floor, so they’re ALWAYS on the elevators), the pipes are a bit loud, there’s not much of a view, and the Italian restaurant attached the hotel (Trestle) has shoddy breakfast service. 4 I wanted to get the phone back but I was also a little nervous, ok a lot nervous, about going to Penn Station Vjust me and my son- to meet a strange man who somehow had my son’s phone (he said he found it on 6th Ave). 5 They addressed my concerns by providing an upgraded room that included a fridge and microwave and even threw in two complimentary vouchers for breakfast at the restaurant attached to the hotel and two cold bottles of water (which we had to drink quickly since there was no fridge to keep them cold!).

6 The bathroom was clean(stocked with bath and body products- one of my favorite brands), bed was extremely plush and comfortable, temperature was perfect(didn’t need to adjust at all) and a 32 inch flat panel HD TV kept me entertained. 7 Pros: clean, friendly, location, price, service Cons: room size.

Table B4 Review summarization results for Wyndham Garden Hotel (Method D). Positive

Negative

1 The bathroom was clean(stocked with bath and body products- one of my favorite brands), bed was extremely plush and comfortable, temperature was perfect(didn’t need to adjust at all) and a 32 inch flat panel HD TV kept me entertained. 2 Excellent location (close to subway, Madison Square Park, Chelsea Market, the Highline Park).

1 I wanted to get the phone back but I was also a little nervous, ok a lot nervous, about going to Penn Station Vjust me and my son- to meet a strange man who somehow had my son’s phone (he said he found it on 6th Ave). 2 There are so many awful hotels in NYC V run down, old, ugly, dirty, and uncomfortable and all of that for a disgustingly expensive rate K but this one is actually a diamond in the rough. 3 When housekeeping accidently threw away my empty water bottle, Lisa showed up at my door within minutes with a brand new, non-disposable water bottle, snacks and breakfast vouchers.

3 I was impressed by the amenities such as the coffee maker, iron and ironing board, hair dryer, and flat screen TV with free Wi-Fi internet.

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Table B4 (continued ) Positive

Negative

4 On check-in Courtney was most friendly and professional- for the rest of BOTH by stays the staff were unresponsive and dismissive- when reporting that I felt unsafe with a safe that was not secured to the wall as I travel with huge amounts of cash. 5 Rooms are a good size with 2 double beds in each, Shower room was a little on the small side but adequate and always plenty of hot water. 6 I thought, Talk about being right ON a construction site while the worker himself is drilling into concrete with100, 000 lbs. of terrifying torque!

4 The lobby is the size of a storefront, there are only eight rooms per floor, room size is cramped, light oak furnishings reminiscent of twenty-thirty years ago, carpet colors selected to hide dirt, drab wallpaper. 5 Shower full of mold, dirty washcloth still in the shower, hair too. 6 As for the hotel review in general, it is as reported: very clean, very small(but the norm for Manhattan), and the elevator was only slow one out of a zillion times that we rode it so not bad.

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Tourism Management 80 (2020) 104122 Kuanchin Chen is a Professor of Computer Information Sys­ tems at Western Michigan University. Dr. Chen’s research in­ terests include electronic business, social networking, project management, privacy & security, online behavioral issues, business analytics and human computer interactions. He has published articles in journals and other academic publication outlets, including Information Systems Journal, Decision Sup­ port Systems, Information & Management, IEEE Transactions on Systems, Man, and Cybernetics, Internet Research, Journal of Database Management, Communications of the Association for Information Systems, Electronic Commerce Research and Applications, Journal of Global Information Management, DATA BASE for Advances in Information Systems, Decision Sciences Journal of Innovative Education, and many others. Dr. Chen serves on the editorial review boards of several academic journals.

Tan, J., Kotov, A., Mohammadiani, R. P., & Huo, Y. (2017). Sentence retrieval with sentiment-specific topical anchoring for review summarization. In ACM conference on information and knowledge management (pp. 2323–2326). Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Annual meeting on association for computational linguistics (pp. 417–424). Wang, D., Zhu, S., & Li, T. (2013). SumView: A web-based engine for summarizing product reviews and customer opinions. Expert Systems with Applications, 40(1), 27–33. Wilson, T., Hoffmann, P., Somasundaran, S., Kessler, J., Wiebe, J., Choi, Y., et al. (2005a). OpinionFinder: A system for subjectivity analysis. Proceedings of HLT/ EMNLP Interactive Demonstrations, 34–35. Wilson, T., Wiebe, J., & Hoffmann, P. (2005b). Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of human language Technology conference and conference on empirical methods in natural language processing. Yang, J.-Y., Myung, J., & Lee, S.-G. (2009). The method for a summarization of product reviews using the user’s opinion. International Conference on Information, Process, and Knowledge Management, 84–89. Zhan, J., Loh, H. T., & Liu, Y. (2009). Gather customer concerns from online product reviews – a text summarization approach. Expert Systems with Applications, 36(2), 2107–2115. Zhu, L., Yin, G., & He, W. (2014). Is this opinion leader’s review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research, 15(4), 267–280.

Ya-Han Hu is currently a Professor of Department of Infor­ mation Management at National Central University, Taiwan. He received a PhD degree in Information Management from National Central University of Taiwan in 2007. His current research interests include text mining and information retrieval, clinical decision support systems, and recommender systems. His research has appeared in Information & Manage­ ment, Decision Support Systems, Journal of the American So­ ciety for Information Science and Technology, IEEE Transactions on Systems, Man, and Cybernetics, International Journal of Information Management, Artificial Intelligence in Medicine, Applied Soft Computing, Computers in Human Behavior, Data & Knowledge Engineering, Expert Systems, Knowledge-Based Systems, Information Systems and e-Busi­ ness Management, Journal of Information Science, Journal of Clinical Epidemiology, Methods of Information in Medicine, Online Information Review, and Journal of Systems and Software.

Chih-Fong Tsai is now a professor at the Department of In­ formation Management, National Central University, Taiwan. He received a PhD degree at School of Computing and Tech­ nology from the University of Sunderland, UK in 2005. He has published more than 30 refereed journal papers including ACM Transactions on Information Systems, Decision Support Systems, Pattern Recognition, Information Processing & Management, Applied Soft Computing, Neurocomputing, Knowledge-Based Sys­ tems, Expert Systems with Applications, Expert Systems, Online Information Review, International Journal on Artificial Intelligence Tools, Journal of Systems and Software, etc. He received the Distinguished New Faculty Award from National Central Uni­ versity in 2010 and the Highly Commended Award (Emerald Literati Network 2008 Awards for Excellence) for a paper published in Online Information Review (“A Review of Image Retrieval Methods for Digital Cultural Heritage Resources”). His current research focuses on multimedia information retrieval and data mining applications.

Wei-Kai Chen received his MS degree in Information Man­ agement from National Chung Cheng University of Taiwan in 2016. His research interests include data mining, information retrieval and EC technologies.

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