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User sentiment analysis based on social network information and its application in consumer reconstruction intention
Accepted Manuscript User Sentiment Analysis Based on Social Network Information and Its Application in Consumer Reconstruction Intention
Qingyuan Zhou, Zheng Xu, Neil Y. Yen PII:
S0747-5632(18)30328-5
DOI:
10.1016/j.chb.2018.07.006
Reference:
CHB 5595
To appear in:
Computers in Human Behavior
Received Date:
10 March 2018
Accepted Date:
03 July 2018
Please cite this article as: Qingyuan Zhou, Zheng Xu, Neil Y. Yen, User Sentiment Analysis Based on Social Network Information and Its Application in Consumer Reconstruction Intention, Computers in Human Behavior (2018), doi: 10.1016/j.chb.2018.07.006
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ACCEPTED MANUSCRIPT
User Sentiment Analysis Based on Social Network Information and Its Application in Consumer Reconstruction Intention Qingyuan Zhou1, 2 , Zheng Xu3 and Neil Y. Yen4 1. School of Economics and Management, Changzhou Vocational Institute of Mechatronic Technology, China 2. Department of Economics and Management, Changzhou Administrative College, China 3. The third research institute of the ministry of public security, Shanghai, China 4. University of Aizu, Japan The corresponding author: Zheng Xu and Qingyuan Zhou, [email protected], [email protected]
ACCEPTED MANUSCRIPT Abstract: Due to the increasingly fierce competition in the consumer goods market, customer retention strategies are of great importance for enterprises to maintain their dominance and long-term and stable earnings. Understanding customer repurchase intention and re-patronage is the prerequisite and foundation for business and retailers. Online reviews include ratings and emotional information from many customers on brands and online stores. Consumer repurchase intention can be measured by mining the attitudes and emotions of consumers in the reviews. Based on online reviews, this work used textual sentiment calculation and fuzzy mathematics to study the online repurchase intention of online consumers. Taking satisfaction, trust and promotion efforts as the antecedents, and consumer repurchase intention as the consequent, a model was established based on emotional computing and fuzzy reasoning. Through the satisfaction, trust and promotion effort of five sportswear brands in Taobao, we verified the reasoning for determining consumer repurchase intention of products. Meanwhile, the relationship between the initial purchase intention and repurchase intention of consumers was compared, thus providing the basis for online stores to formulate their marketing strategy and brand segmentation. Keywords: Social network information; emotion calculation; network consumption decision; fuzzy reasoning 1. Introduction With the rapid development of social network and e-commerce, more and more consumers choose online shopping. The China Internet Network Information Center (CNNIC) report shows that as of June 2016, the number of China’s online shopping users reached 448 million, which increased by 34.48 million compared to the end of 2015, representing an increase of 8.3%. China’s online shopping market still maintains a rapid and steady growth trend. From macro policies to corporate promotions, governments and enterprises are working together to promote trading up. The “13th Five-Year Plan” clearly defines the direction of trading up from the top-level design and emphasizes that the consumption structure should be upgraded with a focus on expanding service consumption to guide consumption toward intelligence, environmental protection, intensification and quality. As a combination of traditional retail and information consumption, online shopping conforms to the trend of new trading up. With the proliferation of various social media outlets, many consumers are increasingly willing to leave their comments on products after online purchase. Product reviews are part of “electronic word-of-mouth,” but tend to be more credible than the publicity from the business. Therefore,
ACCEPTED MANUSCRIPT potential consumers can obtain relevant information before buying products by making full use of the comments. Online reviews can assist shopping decisions and rationalize shopping. According to survey data, the importance of online review is the highest among various factors that can influence purchasing decisions; approximately 83.5% of online shoppers value the opinions from other consumers regarding the product. For online stores and e-commerce platform, it is of great significance for market competitiveness to maintain regular customers, meet customers’ expectations and constantly increase customers’ repurchasing. Therefore, making consumers repeat purchasers is one of the major challenges for network vendors. With the increase in the number of online reviews, the mining of online reviews is gradually emerging. The authors’ subjective tendencies can be observed in the comments. The mining of comments that reflect the attitude of the author is called opinion mining. It mainly involves the following aspects: subjective sentence location, product feature extraction, user’s emotion extraction, emotional polarity judgment, and visualization of mining results. Minqing Hu et al. mined product features based on association rules (Hu & Liu, 2004 a). In terms of the particularity of Chinese, Shi Li et al. performed principle innovation and technology expansion so that they could be applied to Chinese reviews (Li, Ye, Li & Law, 2009). Tianfang Yao et al. used domain ontology to extract the theme and attribute of sentences and obtained the theme’s emotional polarity by combining syntactic analysis (Yao & Lou, 2007). Hatzivassiloglou et al. believed that one of the effective ways to discern subjectivity is through the use of adjectives. When emotional tendencies are extracted and identified in an unsupervised method, the hidden emotional expression units refer to the adverbs, nouns and adjectives in the text (Hatzivassiloglou & McKeown,1997). Mao et al. used the conditional random fields model to mark sentence sentiment, thus judging the sentiment of the article (Mao & Lebanon, 2006). McDonald et al. used the Viterbi algorithm to annotate articles and statements in the model (McDonald, Hannan, Neylon, Wells, & Reynar, 2007). Blair-Goldensohn et al. found that the maximum entropy model method has better accuracy in product attribute mining (Blair-Goldensohn, Hannan, McDonald, Neylon, Reis & Reynar 2008). At present, the use of emotional analysis is limited in online consumers’ behaviors. Therefore, there is a lack of mature theoretical support for studies on consumer behaviors, especially online consumers. In addition, there are few studies on the intention prediction of consumer repurchase behaviors in the network environment. With China’s accession to the World Trade Organization (WTO) and the continuous improvement of China’s market economy, more and more enterprises have joined the competition in the Chinese market (Zhou, 2018). It becomes more and more important to maintain original consumers and relationships with customers. Therefore, increasingly more enterprises have started to shift from market share disputes to existing consumer maintenance (Reichheld & Sasser, 1990). It shows that the repurchase behavior of consumers is crucial for customer retention; customer patronage behavior is equally important for retailer’s customer
ACCEPTED MANUSCRIPT retention and market share maintenance. Behavioral intentions, the predominant variables of behavior, have a strong explanatory power for customer behavior. Therefore, enterprises are very concerned about the consumer repurchase intentions and the retailer’s intention to re-visit. 2. Literature review and research status 2.1 Research of repurchase behavior Repurchase intention is the propensity of consumers to continue participating in retailers or suppliers’ commercial activities (Hennig-Thurau, 2004). Repurchase intention is the focus of academia (Davidow, 2003). Hellier defined it as the individual’s decision to continue purchasing a particular product or service from the same company after considering his/her personal situation. Zeithaml et al. pointed out two manifestations of repurchase intentions: One is that consumers have the idea of buying a particular product or service; the other is that a consumer actively communicates to create positive word of mouth on the product or actively recommends the product or service to others (Zeithaml, Berry, & Parasuraman, 1996). Dixon et al. studied the relationship between purchase intention and actual purchase behavior, as well as the relationship between past and present purchase behaviors (Dixon, Bridson, Evans, & Morrison, 2005). Hennig-Thurau et al. studied consumer repurchase behaviors from the perspective of customer retention (Hennig-Thurau, 2004). Growth in consumer repurchase intention drives profit growth, reduces marketing costs, and makes consumers willing to pay a higher premium for the product (Söderlund, & Vilgon, 1999). Research has shown that the cost of maintaining a regular customer is far less than that of acquiring a new one (Shih & Fang, 2005). Hellier et al. found that the main influencing factors of repurchase behavior are brand loyalty (Hellier, Geursen, Carr, & Rickard, 2003), word-of-mouth communication, complaints, satisfaction and dissatisfaction (Olsen, 2007). Zeithaml and Berry established a structural model of service quality impact on repurchase intention. The model shows that when consumers have a high opinion on the quality of service, they can set up a strong connection with the service provider, leading to strong repurchase intentions. Repurchase intention is an indicator of consumer loyalty—when repurchase intentions are low, consumers often complain and reduce the number of purchases of products or services from suppliers or choose other suppliers (Wang & Yu, 2014). 2.2 Emotional computing research Emotion infiltrates into all aspects of human life. It plays an important role in people’s daily behaviors, affecting people’s decision-making and perception. At present, the studies on affective computation at home and abroad mainly focus on image, sound and physiological information, but text-based affective computing has drawn increasingly more attention from researchers. Text-based sentiment calculation has a wide range of research and practical values in many aspects (Hu, Liu, & Zhang, 2007), such as news comment, public opinion analysis (monitoring and feedback on public opinion information), and information prediction (for hot events, analysis of network information to
ACCEPTED MANUSCRIPT predict future trends and market forecasting). Especially in the field of product reviews, text-based sentiment computing technology can extract product reviews and analyze emotional tendencies, with a summary. (1) Polarity word mining Polarity words refer to emotional words in a sentence and are used to express the attitude and opinion of a commentator. In product reviews, there are four types of words, namely, nouns, adjectives, adverbs and verbs, that can represent the emotion color of consumers. Extracting polarity words is the basic condition for further judging affective tendencies. Hu et al. thought that the feature object (Hu & Liu, 2004 a) and the corresponding opinion words (polarity words) appear nearby (Hu & Liu, 2004 b). Therefore, after obtaining the feature object of evaluation sentences, the adjectives within a certain range are extracted as its corresponding option words. However, this method does not consider syntactic features, and restricts the viewpoints on the adjectives, with low precision and recall rate. Kim and Hovy adopted a similar approach, using adjectives that appear in the distance window as user’s opinion words (Kim & Hovy, 2005). Popescu et al. used textual rules to combine polarity word extraction with feature extraction (Popescu & Etzioni, 2007). With co-occurrence mode, polarity words are extracted by the presupposed feature object-opinion word template (rules). Feng et al. also used a rule-based approach: First, the product features and the corresponding opinion words are marked in the training corpus. Then, syntactic analyzer is used to obtain the grammatical representation rules between the commodity feature and opinion words (Feng, Zhang, Zhang, & Deng, 2010). The evaluation opinions are further extracted through the learned rules. After extracting the opinions, the polarity and the strength of the opinions are extracted. (2) Negative part-of-speech judgment of polarity words The propensity to judge polarity words refers to the judgment of sentiment orientation when the user uses the polarity word, that is, positive or negative attitude. There are two kinds of judgments based on lexicon/corpus. Minqing Hu et al. used WordNet to determine the polarity of adjectives (Hu & Liu, 2004 a). The algorithm first sets the seed polarity word set, containing the most common 30 adjectives with corresponding judge polarities. Then, WordNet is used to find synonyms or antonyms. The method ignores all polarity words other than the adjectives, without considering the different judgments of adjectives in different contexts. Therefore, there are some limitations in the method. The corpus-based approach mainly uses the co-occurrence pattern or modification pattern of the to-be-decided words and reference words to identify the judgment degree of the to-be-judged words. For example, Turney assumed that the polarity words that often appear together are more likely to have the same tendency toward judgment (Turney, 2002). Therefore, sets of negative and positive words are established, each containing seven common polarity words. For the to-be-decided words, pointwise mutual information (PMIs) of two sets are calculated to determine the polarity of the to-
ACCEPTED MANUSCRIPT be-judged words. Wang et al. used the title of a comment to describe the attitude of the entire product review (Wang, Lu, & Zhang, 2005). Therefore, the title is used as a polarity label, and a Bayesian classifier is established to determine the polarities of the words in the product reviews. The current popular topics are understanding and measuring consumers’ intentions of repurchasing from online reviews and the intention to revisit of online merchants, which should be applied to marketing management. The corresponding research on text mining and affective computing is still in its infancy, and the quantitative research on semantics focuses on the accurate measurement of polarity by classical mathematics. However, the natural language of human beings is uncertain, and accurate measurement cannot completely extract consumers’ intentions of behaviors. Therefore, based on the fuzzy and indefinable features of human languages, this work performed the emotional analysis of network evaluation. Based on fuzzy inference rules, we constructed an online consumer repurchase intention model to predict consumer repurchase behaviors. 3. Consumer online repurchase fuzzy model based on online comment 3.1 Fuzzy theory In mathematics and statistics, the set that can be clearly quantified is called a crisp set. It clearly indicates the element that “belongs to” or “does not belong to” the set. The definition is shown as
1, for x A x A ( x) 0, for x A x A : X {0,1}
(1)
In fact, many concepts we usually use have indefinite extension. In other words, there is an uncertain object that meets the concept of the set, or the uncertain set boundary. Therefore, it is impossible for some objects to judge whether they belong to the set. A fuzzy set is introduced to solve the problem (Zadeh, 2008). Let A be a mapping from the domain X to the closet interval [0, 1], that is,
A : X [0,1], x A( x) [0,1]
(2)
Then, A is a fuzzy set (or fuzzy subset) on X, and A( x) is called the membership function of fuzzy set A.
A ( x)
represents the value of A( x) , and
A ( x)
is called the membership of x to A.
To apply fuzzy theory to solve practical problems, fuzzy membership function should be established. The determination of fuzzy membership function is a very crucial issue for the application of fuzzy theory. The work uses Gaussian membership function, whose distribution is
A ( x) exp( where a is the mean of an inverted bell and
( x a ) 2 ) 2 2
(3)
is the scope. Fig. 1 shows the membership function.
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0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 -6
-4
-2
0
2
4
6
Fig. 1 Gaussian membership function 3.2 Influencing factor determination of consumer repurchase intention Repurchase intention indicates the future behavior tendency of consumers. Behavior intention, reflecting the degree of future behavior tendency, is the subjective speculation of certain behaviors. The impacts on consumer repurchase intention mainly include value, satisfaction, brand, etc. Through studying online consumers, Rose et al. found that satisfaction and trust have a direct impact on repurchase intention and that satisfaction indirectly affects repurchase behavior intention through trust. Customer satisfaction is the overall evaluation of consumers on the consumption and use of products and services based on their consumption experience. Its rationale is that the difference between pre-purchase customer expectations and post-purchase perceived performance can lead to changes in customer satisfaction perception. Consumer satisfaction leads to increased consumer repurchase intention, so one of the repurchase intention variables is consumer satisfaction. Trust is a very important adjustment factor in online shopping. Studies show that trust plays an important role in consumer repurchase intention (Zadeh, 2008). Satisfaction and trust are determined by the cognitive experience and emotional experience of online shopping. They affect the consumers repurchase intention that further determines consumer repurchase behavior (Rose, Clark, Samouel, & Hair, 2012). In addition, perceptive behavior control (PBC) is used to describe the influence of external environment and its own conditions on behavior and behavior intention. Research has shown that the pricing strategy varies from brand to brand, and product price can also cause the switch barriers of consumers. Consumer repurchase intention is affected by the relative prices of brands. Therefore, for the perception of behavioral control, it mainly considers the influence of external stimuli on consumer repurchase intention, using the concept of promotional efforts to express. Therefore, the online consumer repurchase behavior is affected by repurchase intention, while the repurchase behavior intention is affected by the consumer’s own emotion and external adjustment factors. Consumer’s emotion is mainly affected by brand satisfaction and trust; for outside adjustment factors, it is mainly affected by product promotion efforts. Fig. 2 shows the
ACCEPTED MANUSCRIPT theoretical model building of consumer repurchase behavior intention.
Online reviews Text extraction
Satisfaction
Trust
Discount
Repurchase intention
Repurchase behavior Fig. 2 Consumers' repurchase intention prediction model It can be found that the studies on consumer repurchase are mainly performed through repurchase intentions that are embodied in the three dimensions of satisfaction, trust and promotion. Online consumers’ satisfaction and trust in the shopping experience are expressed through online reviews, and promotion efforts are expressed through discounts, gifts, etc. The synergy of the three dimensions affects consumer repurchase behavior intention, which further affects consumer actual behaviors. 3.3 Fuzzification of influencing factors Online reviews reflect the psychological state of shopping and comprehensive emotion of online consumers towards products. Therefore, the two dimensions of consumers’ satisfaction and trust are calculated through the mining of online reviews. The calculation steps are as follows: Step 1: Extract online through online review acquisition software and preprocess the text. Choose the complete online reviews that clearly express consumer sentiment to improve accuracy of the sentiment calculation of the review text. Step 2: Perform the word segmentation of web text and make syntactic analysis and keyword extraction of reviews. Use word-level emotional orientation calculation. According to the review emotion corpus as well as part-of-speech tagging and syntactic analysis, obtain the basic emotional review words of consumers including satisfaction and trust. Use review emotional corpus for
ACCEPTED MANUSCRIPT language fuzzification of review emotion. Step 3: Perform joint, interception and complement to words based on collocation, that is, fuzzy membership function is adjusted by centralization, decentralization and negation. Step 4: Using the reviews as units, multi-sentence analysis and calculation are performed on customer’s satisfaction and trust to obtain their fuzzy membership functions. Step 5: Use the centroid method for clarification and obtain the calculation results. 3.4 Establishment of emotional corpus of online review First, the emotional corpus is established according to polarity words. Consumer product experience emotions, based on online reviews, mainly include satisfaction and trust. This work established two types of corpuses based on consumers’ satisfaction and trust support. Combined with the level of emotional words in reviews, the semantic annotation was performed with emotional levels for two kinds of words in terms of different emotional strengths (Zhou, 2018). Consumer online reviews are expressed in the natural language. In the traditional sentiment analysis of natural language semantics computing, the semantics of words are expressed in the exact assignment of classical mathematics. However, regardless of the meaning of semantics or its extension, exact assignment cannot fully express the meaning of words. Na et al. proposed a method to calculate the fuzzy emotion strength of words combined with the semantic study of online review words. According to judgments and emotions, the fuzzy sets are divided into positive, negative and non-polar evaluation intervals. The positive and negative intervals are divided into four evaluation levels
(low,
medium,
high
and
extreme),
and
the
specific
assignment
is
{ VL, L, M, S, Z,S, M, L, VL} {4, 3, 2, 1, 0,1, 2,3, 4} . Here, 4 and -4 are the extremum of the high and low evaluation; 0 is the non-extreme measure. For the selection of fuzzy membership function, we choose the symmetric convex fuzzy set according to the establishment rules of membership function. Gaussian fuzzy membership function is defined on the domain, as shown in Eq. (4).
F ( x) gaussf ( x, w , cw ) exp[
( x cw ) 2 ] 2 w2
（4）
c where w { VL, L, M, S, Z,S, M, L, VL} ; w is the expectation of membership function;
w
is the standard deviation of Gaussian function; and, in general,
value of the element
w 0.4 . A greater absolute
x for w leads to a stronger judgment and polarity as well as higher emotional
intensity of the word. As an important outside stimulus for consumer behavior intentions, promotion effort is affected by the subjective impression of consumers, without fixed division pattern. Therefore, this work classified the promotion efforts of products into three dimensions (high, medium and low). To describe them, the product discount rate p was introduced, defined as
ACCEPTED MANUSCRIPT
p P0 P1 / P0
(5)
P P where 0 p 1 ; 0 is the original price; and 1 is the promotional price. Three different promotion efforts are defined as
{0,1, 2} . When 0 p 1 , c0 0 , indicating a high
c 1 , indicating a medium promotion effort; when promotion effort; when 0.7 p 0.9 , 1 0 p 0.7 , c2 2 , indicating a low promotion effort. The corresponding membership function is
F ( P) gaussf ( p, w , cw ) exp[
( p cw ) 2 ] 2 w2
(6)
where P is the promotion effort, one of the antecedents of fuzzy reasoning. 3.5 Consumer repurchase intention model based on online review The fuzzy reasoning rules are as follows. IF antecedent, THEN conclusion (consequent) The consequent is enhanced with enhanced antecedent. Since the antecedent and consequent of reasoning have fuzzy attributes, the method is fuzzy approximate reasoning. The consequent increases with antecedent. The following assumptions are made: A1: There is a positive correlation between satisfaction and repurchase intention. A2: There is a positive correlation between trust and repurchase intention. A3: There is a positive correlation between product promotion effort and repurchase intention. In this work, the prediction model of consumer repurchase intention (see Fig. 2) took satisfaction, trust and promotion effort as antecedents, and repurchase intention as consequent. The specific form is as follows. IF satisfaction and trust, and discount THEN repurchase A1, A2 and A3 show consumers’ high satisfaction and trust, and greater promotion effort results in greater consumer repurchase intention. The 3D map is obtained according to the reasoning rules (see Fig. 3).
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repurchase
0.5
0
-0.5 2 1
2 1
0
0
-1 trust
-1 -2
-2
satisfaction
Fig. 3 Consumer repurchase intention fuzzy inference rules 4. Case analysis and verification To verify the model performance, this work selected products that consumers frequently bought and commonly used shopping websites to verify the examples. We selected the online reviews in the category of sports apparel in Taobao and studied the consumer repurchase intentions. There are more comments on the products rather than corpus noise. The consumer online comments were separated by a series of operations (such as noise removal, data integration and selection, and the deletion of the HTML identifier in the webpage and some fixed content in the website template). The comments extracted for the web page were converted into a topic structure that is easy for us to handle. Finally, 3,450 valid comments were selected, regarded as comment set A, and classified by the brands. This work targeted product features and attributes of sportswear in Taobao. Based on the consumer psychology, behavior theory and research results of the predecessors, the emotional corpus of sportswear reviews was established. It included emotion experience and trust support thesauruses (corresponding to consumers’ satisfaction and trust), with fuzzy semantic annotation. The online reviews of 5 common sports brands were extracted and calculated with the brand as a unit, thus making the results more accurate. Table 1 shows the extracted number of product reviews. Table 1 Calculation of online reviews Brand
Review Total
Effective Number
Effective Review ratio (%)
Nike
725
703
96.96
Adidas
786
761
96.82
LiNing
763
752
98.56
Anta
623
608
97.59
Reebok
553
543
98.19
From the valid review rate in the table, there is a small rate of noise reviews, which means the
ACCEPTED MANUSCRIPT attitudes of participants are serious and objective. After extracting sentiment words related to the satisfaction and trust of the product in online reviews, Eqs. (4) and (6) are used for the calculation of semantic fuzzification and consumers’ comprehensive sentiment. Fig. 4 shows the calculation results. 1.3
satisfaction trust
1.2
1.1
Satisfaction
1
0.9
0.8
0.7
0.6
0.5
0.4
Nike
Adidas
LiNing Brand
Anta
Reebok
Fig. 4 Calculation results of sportswear It shows that among the five sportswear brands, Li-Ning has the highest trust rating of 1.08,followed by Nike at 1.02; Adidas, Anta and Reebok are lower, at 0.99, 0.83 and 0.78, respectively. Li-Ning is an outstanding sports brand in China, and consumers pay the highest attention to it. In addition, the Li-Ning Group expands sales channels and coverage through industrial sales reform, thus obtaining the highest trust. Compared with two foreign brands, Nike and Adidas, Li-Ning lags behind in the quality dimension. This shows that Li-Ning’s price advantage has attracted more price-sensitive consumers. It has a high cost performance, but the quality and experience satisfaction need to be improved, which is consistent with the market positioning of LiNing. Different from Nike targeting high-end consumers, Li-Ning focuses on the integrity of the product chain. The proposed fuzzy reasoning rules were used to establish the repurchase intention model. After reasoning, the data were calculated for the five sportswear brands (see Table 2). Table 2 Results of sportswear calculation Brand
Satisfaction
Trust
Discount
First purchase
Repurchase
Nike
0.92
1.02
2
1.63
1.76
Adidas
1.2
0.99
1
1.57
0.23
Li-Ning
0.78
1.08
2
1.76
1.85
Anta
0.46
0.78
0
0.76
0.93
Reebok
0.86
0.93
1
0.85
1.56
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The highest repurchase intention is for Li-Ning, and the lowest is for Adidas because there are more Adidas imitation products. This directly affects the brand’s trial experience, resulting in a lower repurchase intention. After the normalization of initial purchase intention and repurchase intention, Fig. 5 shows the data comparison results. 2 Repurchase First purchase
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
Nike
Adidas
LiNing Brand
Anta
Reebok
Fig. 5 First purchase intention and repurchase intention Fig. 5 shows that the five brands can be divided into four types, and Table 3 shows the characteristics and countermeasures of various situations. Table 3 Kinds of sportswear Kind
Brand
First purchase and repurchase are high
Nike and Li-Ning
First purchase is low while repurchase is high
Reebok
First purchase is high while repurchase is low
Adidas
First purchase and repurchase are low
Anta
Features Has good reputation, high satisfaction, loyal consumers Although with a low tendency of consumers to try for the first time, it has high satisfaction and repurchase intention. The brand is alternative; hard to retain loyal repurchase consumers. Insufficient brand competitiveness
Analysis Maintain brand reputation and service quality Strengthen advertising and promotion effort Maintain brand value and crack down on imitation goods on the network. Strengthen brand quality and consumer acceptance
5. Conclusions Based on the online reviews of the products, Internet word-of mouth and information mining,
ACCEPTED MANUSCRIPT this work calculated consumers’ satisfaction and trust and established a fuzzy reasoning model of consumer repurchase intention. Taking satisfaction, trust and promotion efforts as antecedents, and consumer repurchase intention as consequent, fuzzy reasoning was used to obtain consumer repurchase intention. Through the calculation of satisfaction, trust and promotion effort of five sportswear brands in Taobao, we determined the degree of consumer repurchase intention for the products. In addition, this work analyzed the relationship between the initial purchase intention and the repurchase intention of consumers, providing a basis for marketing strategy development and brand segmentation of online stores.
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ACCEPTED MANUSCRIPT (IJCNLP), Jeju Island, Korea, pp. 61–66. Li, S., Ye, Q., Li, Y. J., & Law, R. (2009). Mining features of products from Chinese customer online reviews. Journal of management sciences in China, 12(2): 142-152. Mao, Y., & Lebanon, G. (2006). Isotonic conditional random fields and local sentiment flow. In Advances in Neural Information Processing Systems 19 (NIPS 2006), Edited by B. Schölkopf and J.C. Platt and T. Hoffman. (https://papers.nips.cc/paper/3152-isotonic-conditional-randomfields-and-local-sentiment-flow) McDonald, R., Hannan, K., Neylon, T., Wells, M., & Reynar, J. (2007). Structured models for fineto-coarse sentiment analysis. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pp. 432-439., Prague, Czech Republic, June 2007. Association for Computational Linguistics. Olsen, S. O. (2007). Repurchase loyalty: The role of involvement and satisfaction. Psychology & Marketing, 24(4), 315-341. DOI: https://doi.org/10.1002/mar.20163 Popescu AM., Etzioni O. (2007) Extracting Product Features and Opinions from Reviews. In: Kao A., Poteet S.R. (eds) Natural Language Processing and Text Mining. Springer, London. DOI: https://doi.org/10.1007/978-1-84628-754-1_2 Reichheld, F. F., & Sasser, J. W. (1990). Zero defections: Quality comes to services. Harvard business review, 68(5), 105-111. PMID: 10107082. Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online Customer Experience in e-Retailing: An empirical model of Antecedents and Outcomes. Journal of Retailing, 88(2), 308-322. DOI: https://doi.org/10.1016/j.jretai.2012.03.001 Shih, Y. Y., & Fang, K. (2005). Customer defections analysis: an examination of online bookstores. The TQM Magazine, 17(5), 425-439. DOI: https://doi.org/10.1108/09544780510615933 Söderlund, M., & Vilgon, M. (1999). Customer satisfaction and links to customer profitability: an empirical examination of the association between attitudes and behavior. No 1999:1, SSE/EFI Working Paper Series in Business Administration, Stockholm School of Economics. Turney, P. D. (2002). Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL '02). Association for Computational Linguistics, Stroudsburg, PA, USA, pp. 417-424. DOI: https://doi.org/10.3115/1073083.1073153 Wang, C., Lu, J., & Zhang, G. (2005). A semantic classification approach for online product reviews. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05), 2005, pp. 276-279. DOI: 10.1109/WI.2005.12 Wang, C., & Yu, W. (2014). Study on Corporate Social Responsibility (CSR) Affect on Brand Trust and Purchase Intention After Brand Scandal. In: Xu J., Fry J., Lev B., Hajiyev A. (eds) Proceedings of the Seventh International Conference on Management Science and Engineering Management. Lecture Notes in Electrical Engineering, vol 241, pp. 283-290. Springer, Berlin, Heidelberg. DOI: https://doi.org/10.1007/978-3-642-40078-0_24 Yao, T. F., & Lou, D. C. (2007). Research on semantic orientation analysis for topics in Chinese sentences. Journal of Chinese Information Processing, 21(5), 73-79. Zadeh, L. A. (2008) A Fuzzy-Set-Theoretic Interpretation of Linguistic Hedges, Journal of Cybernetics, 2:3, 4-34. DOI: 10.1080/01969727208542910 Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46. DOI: 10.2307/1251929 Zhou, Q. (2016). Research on heterogeneous data integration model of group enterprise based on cluster computing. Cluster Computing, 19(3), 1275-1282. DOI: https://doi.org/10.1007/s10586-
ACCEPTED MANUSCRIPT 016-0580-y Zhou, Q. (2018). Multi-layer affective computing model based on emotional psychology. Electronic Commerce Research, 18(1), 109-124. DOI: https://doi.org/10.1007/s10660-017-9265-8
ACCEPTED MANUSCRIPT Highlights:
Online reviews include many customers’ ratings and emotional information. It can measure consumer repurchase intentions by mining the attitudes and emotions. The model was established based on emotional computing and fuzzy reasoning. Relationship between the initial purchase and the repurchase intention of consumers.
Qingyuan Zhou, Zheng Xu, Neil Y. Yen PII:
S0747-5632(18)30328-5
DOI:
10.1016/j.chb.2018.07.006
Reference:
CHB 5595
To appear in:
Computers in Human Behavior
Received Date:
10 March 2018
Accepted Date:
03 July 2018
Please cite this article as: Qingyuan Zhou, Zheng Xu, Neil Y. Yen, User Sentiment Analysis Based on Social Network Information and Its Application in Consumer Reconstruction Intention, Computers in Human Behavior (2018), doi: 10.1016/j.chb.2018.07.006
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ACCEPTED MANUSCRIPT
User Sentiment Analysis Based on Social Network Information and Its Application in Consumer Reconstruction Intention Qingyuan Zhou1, 2 , Zheng Xu3 and Neil Y. Yen4 1. School of Economics and Management, Changzhou Vocational Institute of Mechatronic Technology, China 2. Department of Economics and Management, Changzhou Administrative College, China 3. The third research institute of the ministry of public security, Shanghai, China 4. University of Aizu, Japan The corresponding author: Zheng Xu and Qingyuan Zhou, [email protected], [email protected]
ACCEPTED MANUSCRIPT Abstract: Due to the increasingly fierce competition in the consumer goods market, customer retention strategies are of great importance for enterprises to maintain their dominance and long-term and stable earnings. Understanding customer repurchase intention and re-patronage is the prerequisite and foundation for business and retailers. Online reviews include ratings and emotional information from many customers on brands and online stores. Consumer repurchase intention can be measured by mining the attitudes and emotions of consumers in the reviews. Based on online reviews, this work used textual sentiment calculation and fuzzy mathematics to study the online repurchase intention of online consumers. Taking satisfaction, trust and promotion efforts as the antecedents, and consumer repurchase intention as the consequent, a model was established based on emotional computing and fuzzy reasoning. Through the satisfaction, trust and promotion effort of five sportswear brands in Taobao, we verified the reasoning for determining consumer repurchase intention of products. Meanwhile, the relationship between the initial purchase intention and repurchase intention of consumers was compared, thus providing the basis for online stores to formulate their marketing strategy and brand segmentation. Keywords: Social network information; emotion calculation; network consumption decision; fuzzy reasoning 1. Introduction With the rapid development of social network and e-commerce, more and more consumers choose online shopping. The China Internet Network Information Center (CNNIC) report shows that as of June 2016, the number of China’s online shopping users reached 448 million, which increased by 34.48 million compared to the end of 2015, representing an increase of 8.3%. China’s online shopping market still maintains a rapid and steady growth trend. From macro policies to corporate promotions, governments and enterprises are working together to promote trading up. The “13th Five-Year Plan” clearly defines the direction of trading up from the top-level design and emphasizes that the consumption structure should be upgraded with a focus on expanding service consumption to guide consumption toward intelligence, environmental protection, intensification and quality. As a combination of traditional retail and information consumption, online shopping conforms to the trend of new trading up. With the proliferation of various social media outlets, many consumers are increasingly willing to leave their comments on products after online purchase. Product reviews are part of “electronic word-of-mouth,” but tend to be more credible than the publicity from the business. Therefore,
ACCEPTED MANUSCRIPT potential consumers can obtain relevant information before buying products by making full use of the comments. Online reviews can assist shopping decisions and rationalize shopping. According to survey data, the importance of online review is the highest among various factors that can influence purchasing decisions; approximately 83.5% of online shoppers value the opinions from other consumers regarding the product. For online stores and e-commerce platform, it is of great significance for market competitiveness to maintain regular customers, meet customers’ expectations and constantly increase customers’ repurchasing. Therefore, making consumers repeat purchasers is one of the major challenges for network vendors. With the increase in the number of online reviews, the mining of online reviews is gradually emerging. The authors’ subjective tendencies can be observed in the comments. The mining of comments that reflect the attitude of the author is called opinion mining. It mainly involves the following aspects: subjective sentence location, product feature extraction, user’s emotion extraction, emotional polarity judgment, and visualization of mining results. Minqing Hu et al. mined product features based on association rules (Hu & Liu, 2004 a). In terms of the particularity of Chinese, Shi Li et al. performed principle innovation and technology expansion so that they could be applied to Chinese reviews (Li, Ye, Li & Law, 2009). Tianfang Yao et al. used domain ontology to extract the theme and attribute of sentences and obtained the theme’s emotional polarity by combining syntactic analysis (Yao & Lou, 2007). Hatzivassiloglou et al. believed that one of the effective ways to discern subjectivity is through the use of adjectives. When emotional tendencies are extracted and identified in an unsupervised method, the hidden emotional expression units refer to the adverbs, nouns and adjectives in the text (Hatzivassiloglou & McKeown,1997). Mao et al. used the conditional random fields model to mark sentence sentiment, thus judging the sentiment of the article (Mao & Lebanon, 2006). McDonald et al. used the Viterbi algorithm to annotate articles and statements in the model (McDonald, Hannan, Neylon, Wells, & Reynar, 2007). Blair-Goldensohn et al. found that the maximum entropy model method has better accuracy in product attribute mining (Blair-Goldensohn, Hannan, McDonald, Neylon, Reis & Reynar 2008). At present, the use of emotional analysis is limited in online consumers’ behaviors. Therefore, there is a lack of mature theoretical support for studies on consumer behaviors, especially online consumers. In addition, there are few studies on the intention prediction of consumer repurchase behaviors in the network environment. With China’s accession to the World Trade Organization (WTO) and the continuous improvement of China’s market economy, more and more enterprises have joined the competition in the Chinese market (Zhou, 2018). It becomes more and more important to maintain original consumers and relationships with customers. Therefore, increasingly more enterprises have started to shift from market share disputes to existing consumer maintenance (Reichheld & Sasser, 1990). It shows that the repurchase behavior of consumers is crucial for customer retention; customer patronage behavior is equally important for retailer’s customer
ACCEPTED MANUSCRIPT retention and market share maintenance. Behavioral intentions, the predominant variables of behavior, have a strong explanatory power for customer behavior. Therefore, enterprises are very concerned about the consumer repurchase intentions and the retailer’s intention to re-visit. 2. Literature review and research status 2.1 Research of repurchase behavior Repurchase intention is the propensity of consumers to continue participating in retailers or suppliers’ commercial activities (Hennig-Thurau, 2004). Repurchase intention is the focus of academia (Davidow, 2003). Hellier defined it as the individual’s decision to continue purchasing a particular product or service from the same company after considering his/her personal situation. Zeithaml et al. pointed out two manifestations of repurchase intentions: One is that consumers have the idea of buying a particular product or service; the other is that a consumer actively communicates to create positive word of mouth on the product or actively recommends the product or service to others (Zeithaml, Berry, & Parasuraman, 1996). Dixon et al. studied the relationship between purchase intention and actual purchase behavior, as well as the relationship between past and present purchase behaviors (Dixon, Bridson, Evans, & Morrison, 2005). Hennig-Thurau et al. studied consumer repurchase behaviors from the perspective of customer retention (Hennig-Thurau, 2004). Growth in consumer repurchase intention drives profit growth, reduces marketing costs, and makes consumers willing to pay a higher premium for the product (Söderlund, & Vilgon, 1999). Research has shown that the cost of maintaining a regular customer is far less than that of acquiring a new one (Shih & Fang, 2005). Hellier et al. found that the main influencing factors of repurchase behavior are brand loyalty (Hellier, Geursen, Carr, & Rickard, 2003), word-of-mouth communication, complaints, satisfaction and dissatisfaction (Olsen, 2007). Zeithaml and Berry established a structural model of service quality impact on repurchase intention. The model shows that when consumers have a high opinion on the quality of service, they can set up a strong connection with the service provider, leading to strong repurchase intentions. Repurchase intention is an indicator of consumer loyalty—when repurchase intentions are low, consumers often complain and reduce the number of purchases of products or services from suppliers or choose other suppliers (Wang & Yu, 2014). 2.2 Emotional computing research Emotion infiltrates into all aspects of human life. It plays an important role in people’s daily behaviors, affecting people’s decision-making and perception. At present, the studies on affective computation at home and abroad mainly focus on image, sound and physiological information, but text-based affective computing has drawn increasingly more attention from researchers. Text-based sentiment calculation has a wide range of research and practical values in many aspects (Hu, Liu, & Zhang, 2007), such as news comment, public opinion analysis (monitoring and feedback on public opinion information), and information prediction (for hot events, analysis of network information to
ACCEPTED MANUSCRIPT predict future trends and market forecasting). Especially in the field of product reviews, text-based sentiment computing technology can extract product reviews and analyze emotional tendencies, with a summary. (1) Polarity word mining Polarity words refer to emotional words in a sentence and are used to express the attitude and opinion of a commentator. In product reviews, there are four types of words, namely, nouns, adjectives, adverbs and verbs, that can represent the emotion color of consumers. Extracting polarity words is the basic condition for further judging affective tendencies. Hu et al. thought that the feature object (Hu & Liu, 2004 a) and the corresponding opinion words (polarity words) appear nearby (Hu & Liu, 2004 b). Therefore, after obtaining the feature object of evaluation sentences, the adjectives within a certain range are extracted as its corresponding option words. However, this method does not consider syntactic features, and restricts the viewpoints on the adjectives, with low precision and recall rate. Kim and Hovy adopted a similar approach, using adjectives that appear in the distance window as user’s opinion words (Kim & Hovy, 2005). Popescu et al. used textual rules to combine polarity word extraction with feature extraction (Popescu & Etzioni, 2007). With co-occurrence mode, polarity words are extracted by the presupposed feature object-opinion word template (rules). Feng et al. also used a rule-based approach: First, the product features and the corresponding opinion words are marked in the training corpus. Then, syntactic analyzer is used to obtain the grammatical representation rules between the commodity feature and opinion words (Feng, Zhang, Zhang, & Deng, 2010). The evaluation opinions are further extracted through the learned rules. After extracting the opinions, the polarity and the strength of the opinions are extracted. (2) Negative part-of-speech judgment of polarity words The propensity to judge polarity words refers to the judgment of sentiment orientation when the user uses the polarity word, that is, positive or negative attitude. There are two kinds of judgments based on lexicon/corpus. Minqing Hu et al. used WordNet to determine the polarity of adjectives (Hu & Liu, 2004 a). The algorithm first sets the seed polarity word set, containing the most common 30 adjectives with corresponding judge polarities. Then, WordNet is used to find synonyms or antonyms. The method ignores all polarity words other than the adjectives, without considering the different judgments of adjectives in different contexts. Therefore, there are some limitations in the method. The corpus-based approach mainly uses the co-occurrence pattern or modification pattern of the to-be-decided words and reference words to identify the judgment degree of the to-be-judged words. For example, Turney assumed that the polarity words that often appear together are more likely to have the same tendency toward judgment (Turney, 2002). Therefore, sets of negative and positive words are established, each containing seven common polarity words. For the to-be-decided words, pointwise mutual information (PMIs) of two sets are calculated to determine the polarity of the to-
ACCEPTED MANUSCRIPT be-judged words. Wang et al. used the title of a comment to describe the attitude of the entire product review (Wang, Lu, & Zhang, 2005). Therefore, the title is used as a polarity label, and a Bayesian classifier is established to determine the polarities of the words in the product reviews. The current popular topics are understanding and measuring consumers’ intentions of repurchasing from online reviews and the intention to revisit of online merchants, which should be applied to marketing management. The corresponding research on text mining and affective computing is still in its infancy, and the quantitative research on semantics focuses on the accurate measurement of polarity by classical mathematics. However, the natural language of human beings is uncertain, and accurate measurement cannot completely extract consumers’ intentions of behaviors. Therefore, based on the fuzzy and indefinable features of human languages, this work performed the emotional analysis of network evaluation. Based on fuzzy inference rules, we constructed an online consumer repurchase intention model to predict consumer repurchase behaviors. 3. Consumer online repurchase fuzzy model based on online comment 3.1 Fuzzy theory In mathematics and statistics, the set that can be clearly quantified is called a crisp set. It clearly indicates the element that “belongs to” or “does not belong to” the set. The definition is shown as
1, for x A x A ( x) 0, for x A x A : X {0,1}
(1)
In fact, many concepts we usually use have indefinite extension. In other words, there is an uncertain object that meets the concept of the set, or the uncertain set boundary. Therefore, it is impossible for some objects to judge whether they belong to the set. A fuzzy set is introduced to solve the problem (Zadeh, 2008). Let A be a mapping from the domain X to the closet interval [0, 1], that is,
A : X [0,1], x A( x) [0,1]
(2)
Then, A is a fuzzy set (or fuzzy subset) on X, and A( x) is called the membership function of fuzzy set A.
A ( x)
represents the value of A( x) , and
A ( x)
is called the membership of x to A.
To apply fuzzy theory to solve practical problems, fuzzy membership function should be established. The determination of fuzzy membership function is a very crucial issue for the application of fuzzy theory. The work uses Gaussian membership function, whose distribution is
A ( x) exp( where a is the mean of an inverted bell and
( x a ) 2 ) 2 2
(3)
is the scope. Fig. 1 shows the membership function.
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0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 0 -6
-4
-2
0
2
4
6
Fig. 1 Gaussian membership function 3.2 Influencing factor determination of consumer repurchase intention Repurchase intention indicates the future behavior tendency of consumers. Behavior intention, reflecting the degree of future behavior tendency, is the subjective speculation of certain behaviors. The impacts on consumer repurchase intention mainly include value, satisfaction, brand, etc. Through studying online consumers, Rose et al. found that satisfaction and trust have a direct impact on repurchase intention and that satisfaction indirectly affects repurchase behavior intention through trust. Customer satisfaction is the overall evaluation of consumers on the consumption and use of products and services based on their consumption experience. Its rationale is that the difference between pre-purchase customer expectations and post-purchase perceived performance can lead to changes in customer satisfaction perception. Consumer satisfaction leads to increased consumer repurchase intention, so one of the repurchase intention variables is consumer satisfaction. Trust is a very important adjustment factor in online shopping. Studies show that trust plays an important role in consumer repurchase intention (Zadeh, 2008). Satisfaction and trust are determined by the cognitive experience and emotional experience of online shopping. They affect the consumers repurchase intention that further determines consumer repurchase behavior (Rose, Clark, Samouel, & Hair, 2012). In addition, perceptive behavior control (PBC) is used to describe the influence of external environment and its own conditions on behavior and behavior intention. Research has shown that the pricing strategy varies from brand to brand, and product price can also cause the switch barriers of consumers. Consumer repurchase intention is affected by the relative prices of brands. Therefore, for the perception of behavioral control, it mainly considers the influence of external stimuli on consumer repurchase intention, using the concept of promotional efforts to express. Therefore, the online consumer repurchase behavior is affected by repurchase intention, while the repurchase behavior intention is affected by the consumer’s own emotion and external adjustment factors. Consumer’s emotion is mainly affected by brand satisfaction and trust; for outside adjustment factors, it is mainly affected by product promotion efforts. Fig. 2 shows the
ACCEPTED MANUSCRIPT theoretical model building of consumer repurchase behavior intention.
Online reviews Text extraction
Satisfaction
Trust
Discount
Repurchase intention
Repurchase behavior Fig. 2 Consumers' repurchase intention prediction model It can be found that the studies on consumer repurchase are mainly performed through repurchase intentions that are embodied in the three dimensions of satisfaction, trust and promotion. Online consumers’ satisfaction and trust in the shopping experience are expressed through online reviews, and promotion efforts are expressed through discounts, gifts, etc. The synergy of the three dimensions affects consumer repurchase behavior intention, which further affects consumer actual behaviors. 3.3 Fuzzification of influencing factors Online reviews reflect the psychological state of shopping and comprehensive emotion of online consumers towards products. Therefore, the two dimensions of consumers’ satisfaction and trust are calculated through the mining of online reviews. The calculation steps are as follows: Step 1: Extract online through online review acquisition software and preprocess the text. Choose the complete online reviews that clearly express consumer sentiment to improve accuracy of the sentiment calculation of the review text. Step 2: Perform the word segmentation of web text and make syntactic analysis and keyword extraction of reviews. Use word-level emotional orientation calculation. According to the review emotion corpus as well as part-of-speech tagging and syntactic analysis, obtain the basic emotional review words of consumers including satisfaction and trust. Use review emotional corpus for
ACCEPTED MANUSCRIPT language fuzzification of review emotion. Step 3: Perform joint, interception and complement to words based on collocation, that is, fuzzy membership function is adjusted by centralization, decentralization and negation. Step 4: Using the reviews as units, multi-sentence analysis and calculation are performed on customer’s satisfaction and trust to obtain their fuzzy membership functions. Step 5: Use the centroid method for clarification and obtain the calculation results. 3.4 Establishment of emotional corpus of online review First, the emotional corpus is established according to polarity words. Consumer product experience emotions, based on online reviews, mainly include satisfaction and trust. This work established two types of corpuses based on consumers’ satisfaction and trust support. Combined with the level of emotional words in reviews, the semantic annotation was performed with emotional levels for two kinds of words in terms of different emotional strengths (Zhou, 2018). Consumer online reviews are expressed in the natural language. In the traditional sentiment analysis of natural language semantics computing, the semantics of words are expressed in the exact assignment of classical mathematics. However, regardless of the meaning of semantics or its extension, exact assignment cannot fully express the meaning of words. Na et al. proposed a method to calculate the fuzzy emotion strength of words combined with the semantic study of online review words. According to judgments and emotions, the fuzzy sets are divided into positive, negative and non-polar evaluation intervals. The positive and negative intervals are divided into four evaluation levels
(low,
medium,
high
and
extreme),
and
the
specific
assignment
is
{ VL, L, M, S, Z,S, M, L, VL} {4, 3, 2, 1, 0,1, 2,3, 4} . Here, 4 and -4 are the extremum of the high and low evaluation; 0 is the non-extreme measure. For the selection of fuzzy membership function, we choose the symmetric convex fuzzy set according to the establishment rules of membership function. Gaussian fuzzy membership function is defined on the domain, as shown in Eq. (4).
F ( x) gaussf ( x, w , cw ) exp[
( x cw ) 2 ] 2 w2
（4）
c where w { VL, L, M, S, Z,S, M, L, VL} ; w is the expectation of membership function;
w
is the standard deviation of Gaussian function; and, in general,
value of the element
w 0.4 . A greater absolute
x for w leads to a stronger judgment and polarity as well as higher emotional
intensity of the word. As an important outside stimulus for consumer behavior intentions, promotion effort is affected by the subjective impression of consumers, without fixed division pattern. Therefore, this work classified the promotion efforts of products into three dimensions (high, medium and low). To describe them, the product discount rate p was introduced, defined as
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p P0 P1 / P0
(5)
P P where 0 p 1 ; 0 is the original price; and 1 is the promotional price. Three different promotion efforts are defined as
{0,1, 2} . When 0 p 1 , c0 0 , indicating a high
c 1 , indicating a medium promotion effort; when promotion effort; when 0.7 p 0.9 , 1 0 p 0.7 , c2 2 , indicating a low promotion effort. The corresponding membership function is
F ( P) gaussf ( p, w , cw ) exp[
( p cw ) 2 ] 2 w2
(6)
where P is the promotion effort, one of the antecedents of fuzzy reasoning. 3.5 Consumer repurchase intention model based on online review The fuzzy reasoning rules are as follows. IF antecedent, THEN conclusion (consequent) The consequent is enhanced with enhanced antecedent. Since the antecedent and consequent of reasoning have fuzzy attributes, the method is fuzzy approximate reasoning. The consequent increases with antecedent. The following assumptions are made: A1: There is a positive correlation between satisfaction and repurchase intention. A2: There is a positive correlation between trust and repurchase intention. A3: There is a positive correlation between product promotion effort and repurchase intention. In this work, the prediction model of consumer repurchase intention (see Fig. 2) took satisfaction, trust and promotion effort as antecedents, and repurchase intention as consequent. The specific form is as follows. IF satisfaction and trust, and discount THEN repurchase A1, A2 and A3 show consumers’ high satisfaction and trust, and greater promotion effort results in greater consumer repurchase intention. The 3D map is obtained according to the reasoning rules (see Fig. 3).
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repurchase
0.5
0
-0.5 2 1
2 1
0
0
-1 trust
-1 -2
-2
satisfaction
Fig. 3 Consumer repurchase intention fuzzy inference rules 4. Case analysis and verification To verify the model performance, this work selected products that consumers frequently bought and commonly used shopping websites to verify the examples. We selected the online reviews in the category of sports apparel in Taobao and studied the consumer repurchase intentions. There are more comments on the products rather than corpus noise. The consumer online comments were separated by a series of operations (such as noise removal, data integration and selection, and the deletion of the HTML identifier in the webpage and some fixed content in the website template). The comments extracted for the web page were converted into a topic structure that is easy for us to handle. Finally, 3,450 valid comments were selected, regarded as comment set A, and classified by the brands. This work targeted product features and attributes of sportswear in Taobao. Based on the consumer psychology, behavior theory and research results of the predecessors, the emotional corpus of sportswear reviews was established. It included emotion experience and trust support thesauruses (corresponding to consumers’ satisfaction and trust), with fuzzy semantic annotation. The online reviews of 5 common sports brands were extracted and calculated with the brand as a unit, thus making the results more accurate. Table 1 shows the extracted number of product reviews. Table 1 Calculation of online reviews Brand
Review Total
Effective Number
Effective Review ratio (%)
Nike
725
703
96.96
Adidas
786
761
96.82
LiNing
763
752
98.56
Anta
623
608
97.59
Reebok
553
543
98.19
From the valid review rate in the table, there is a small rate of noise reviews, which means the
ACCEPTED MANUSCRIPT attitudes of participants are serious and objective. After extracting sentiment words related to the satisfaction and trust of the product in online reviews, Eqs. (4) and (6) are used for the calculation of semantic fuzzification and consumers’ comprehensive sentiment. Fig. 4 shows the calculation results. 1.3
satisfaction trust
1.2
1.1
Satisfaction
1
0.9
0.8
0.7
0.6
0.5
0.4
Nike
Adidas
LiNing Brand
Anta
Reebok
Fig. 4 Calculation results of sportswear It shows that among the five sportswear brands, Li-Ning has the highest trust rating of 1.08,followed by Nike at 1.02; Adidas, Anta and Reebok are lower, at 0.99, 0.83 and 0.78, respectively. Li-Ning is an outstanding sports brand in China, and consumers pay the highest attention to it. In addition, the Li-Ning Group expands sales channels and coverage through industrial sales reform, thus obtaining the highest trust. Compared with two foreign brands, Nike and Adidas, Li-Ning lags behind in the quality dimension. This shows that Li-Ning’s price advantage has attracted more price-sensitive consumers. It has a high cost performance, but the quality and experience satisfaction need to be improved, which is consistent with the market positioning of LiNing. Different from Nike targeting high-end consumers, Li-Ning focuses on the integrity of the product chain. The proposed fuzzy reasoning rules were used to establish the repurchase intention model. After reasoning, the data were calculated for the five sportswear brands (see Table 2). Table 2 Results of sportswear calculation Brand
Satisfaction
Trust
Discount
First purchase
Repurchase
Nike
0.92
1.02
2
1.63
1.76
Adidas
1.2
0.99
1
1.57
0.23
Li-Ning
0.78
1.08
2
1.76
1.85
Anta
0.46
0.78
0
0.76
0.93
Reebok
0.86
0.93
1
0.85
1.56
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The highest repurchase intention is for Li-Ning, and the lowest is for Adidas because there are more Adidas imitation products. This directly affects the brand’s trial experience, resulting in a lower repurchase intention. After the normalization of initial purchase intention and repurchase intention, Fig. 5 shows the data comparison results. 2 Repurchase First purchase
1.8
1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
Nike
Adidas
LiNing Brand
Anta
Reebok
Fig. 5 First purchase intention and repurchase intention Fig. 5 shows that the five brands can be divided into four types, and Table 3 shows the characteristics and countermeasures of various situations. Table 3 Kinds of sportswear Kind
Brand
First purchase and repurchase are high
Nike and Li-Ning
First purchase is low while repurchase is high
Reebok
First purchase is high while repurchase is low
Adidas
First purchase and repurchase are low
Anta
Features Has good reputation, high satisfaction, loyal consumers Although with a low tendency of consumers to try for the first time, it has high satisfaction and repurchase intention. The brand is alternative; hard to retain loyal repurchase consumers. Insufficient brand competitiveness
Analysis Maintain brand reputation and service quality Strengthen advertising and promotion effort Maintain brand value and crack down on imitation goods on the network. Strengthen brand quality and consumer acceptance
5. Conclusions Based on the online reviews of the products, Internet word-of mouth and information mining,
ACCEPTED MANUSCRIPT this work calculated consumers’ satisfaction and trust and established a fuzzy reasoning model of consumer repurchase intention. Taking satisfaction, trust and promotion efforts as antecedents, and consumer repurchase intention as consequent, fuzzy reasoning was used to obtain consumer repurchase intention. Through the calculation of satisfaction, trust and promotion effort of five sportswear brands in Taobao, we determined the degree of consumer repurchase intention for the products. In addition, this work analyzed the relationship between the initial purchase intention and the repurchase intention of consumers, providing a basis for marketing strategy development and brand segmentation of online stores.
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ACCEPTED MANUSCRIPT Highlights:
Online reviews include many customers’ ratings and emotional information. It can measure consumer repurchase intentions by mining the attitudes and emotions. The model was established based on emotional computing and fuzzy reasoning. Relationship between the initial purchase and the repurchase intention of consumers.