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Feature selection for helpfulness prediction of online product reviews: An empirical study


Autoři: Jiahua Du aff001;  Jia Rong aff001;  Sandra Michalska aff001;  Hua Wang aff001;  Yanchun Zhang aff001
Působiště autorů: Institute of Sustainable Industries & Liveable Cities, Victoria University, Melbourne, VIC, Australia aff001;  Faculty of Information Technology, Monash University, Clayton, VIC, Australia aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0226902

Souhrn

Online product reviews underpin nearly all e-shopping activities. The high volume of data, as well as various online review quality, puts growing pressure on automated approaches for informative content prioritization. Despite a substantial body of literature on review helpfulness prediction, the rationale behind specific feature selection is largely under-studied. Also, the current works tend to concentrate on domain- and/or platform-dependent feature curation, lacking wider generalization. Moreover, the issue of result comparability and reproducibility occurs due to frequent data and source code unavailability. This study addresses the gaps through the most comprehensive feature identification, evaluation, and selection. To this end, the 30 most frequently used content-based features are first identified from 149 relevant research papers and grouped into five coherent categories. The features are then selected to perform helpfulness prediction on six domains of the largest publicly available Amazon 5-core dataset. Three scenarios for feature selection are considered: (i) individual features, (ii) features within each category, and (iii) all features. Empirical results demonstrate that semantics plays a dominant role in predicting informative reviews, followed by sentiment, and other features. Finally, feature combination patterns and selection guidelines across domains are summarized to enhance customer experience in today’s prevalent e-commerce environment. The computational framework for helpfulness prediction used in the study have been released to facilitate result comparability and reproducibility.

Klíčová slova:

Reproducibility – Linguistic morphology – Grammar – Syntax – Semantics – Metadata – Vocabulary – Lexicons


Zdroje

1. BrightLocal. Local Consumer Review Survey; 2016. Available from: https://www.brightlocal.com/learn/local-consumer-review-survey-2016/.

2. Momeni E, Cardie C, Diakopoulos N. How to Assess and Rank User-Generated Content on Web. In: Companion Proceedings of the The Web Conference 2018. WWW’18. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee; 2018. p. 489–493. Available from: https://doi.org/10.1145/3184558.3186239.

3. Askalidis G, Malthouse EC. The Value of Online Customer Reviews. In: Proceedings of the 10th ACM Conference on Recommender Systems. RecSys’16. New York, NY, USA: ACM; 2016. p. 155–158. Available from: http://doi.acm.org/10.1145/2959100.2959181.

4. Ocampo Diaz G, Ng V. Modeling and Prediction of Online Product Review Helpfulness: A Survey. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Melbourne, Australia: Association for Computational Linguistics; 2018. p. 698–708. Available from: http://www.aclweb.org/anthology/P18-1065.

5. Kim SM, Pantel P, Chklovski T, Pennacchiotti M. Automatically Assessing Review Helpfulness. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. EMNLP’06. Stroudsburg, PA, USA: Association for Computational Linguistics; 2006. p. 423–430. Available from: http://dl.acm.org/citation.cfm?id=1610075.1610135.

6. Li M, Huang L, Tan CH, Wei KK. Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features. International Journal of Electronic Commerce. 2013;17(4):101–136. doi: 10.2753/JEC1086-4415170404

7. Martin L, Pu P. Prediction of Helpful Reviews Using Emotions Extraction. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence. AAAI’14. AAAI Press; 2014. p. 1551–1557. Available from: http://dl.acm.org/citation.cfm?id=2892753.2892768.

8. Yang Y, Yan Y, Qiu M, Bao F. Semantic Analysis and Helpfulness Prediction of Text for Online Product Reviews. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Beijing, China: Association for Computational Linguistics; 2015. p. 38–44. Available from: http://www.aclweb.org/anthology/P15-2007.

9. Krishnamoorthy S. Linguistic features for review helpfulness prediction. Expert Systems with Applications. 2015;42(7):3751–3759. https://doi.org/10.1016/j.eswa.2014.12.044.

10. Malik MSI, Hussain A. Helpfulness of product reviews as a function of discrete positive and negative emotions. Computers in Human Behavior. 2017;73:290–302. https://doi.org/10.1016/j.chb.2017.03.053.

11. Cheng YH, Ho HY. Social influence’s impact on reader perceptions of online reviews. Journal of Business Research. 2015;68(4):883–887. https://doi.org/10.1016/j.jbusres.2014.11.046.

12. Hu YH, Chen K. Predicting hotel review helpfulness: The impact of review visibility, and interaction between hotel stars and review ratings. International Journal of Information Management. 2016;36(6, Part A):929–944. https://doi.org/10.1016/j.ijinfomgt.2016.06.003.

13. Lu Y, Tsaparas P, Ntoulas A, Polanyi L. Exploiting Social Context for Review Quality Prediction. In: Proceedings of the 19th International Conference on World Wide Web. WWW’10. New York, NY, USA: ACM; 2010. p. 691–700. Available from: http://doi.acm.org/10.1145/1772690.1772761.

14. Tang J, Gao H, Hu X, Liu H. Context-aware Review Helpfulness Rating Prediction. In: Proceedings of the 7th ACM Conference on Recommender Systems. RecSys’13. New York, NY, USA: ACM; 2013. p. 1–8. Available from: http://doi.acm.org/10.1145/2507157.2507183.

15. Zhu L, Yin G, He W. Is this opinion leader’s review useful? Peripheral cues for online review helpfulness. Journal of Electronic Commerce Research. 2014;15(4):267.

16. Fang B, Ye Q, Kucukusta D, Law R. Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management. 2016;52:498–506. https://doi.org/10.1016/j.tourman.2015.07.018.

17. Willemsen LM, Neijens PC, Bronner F, de Ridder JA. “Highly Recommended!” The Content Characteristics and Perceived Usefulness of Online Consumer Reviews. Journal of Computer-Mediated Communication. 2011;17(1):19–38. doi: 10.1111/j.1083-6101.2011.01551.x

18. Kuan K, Hui KL, Prasarnphanich P, Lai HY. What Makes a Review Voted? An Empirical Investigation of Review Voting in Online Review Systems. Journal of the Association for Information Systems. 2015;16:48–71. doi: 10.17705/1jais.00386

19. Yang SB, Shin SH, Joun Y, Koo C. Exploring the comparative importance of online hotel reviews’ heuristic attributes in review helpfulness: a conjoint analysis approach. Journal of Travel & Tourism Marketing. 2017;34(7):963–985. doi: 10.1080/10548408.2016.1251872

20. Karimi S, Wang F. Online review helpfulness: Impact of reviewer profile image. Decision Support Systems. 2017;96:39–48. https://doi.org/10.1016/j.dss.2017.02.001.

21. Kwok L, Xie KL. Factors contributing to the helpfulness of online hotel reviews: Does manager response play a role? International Journal of Contemporary Hospitality Management. 2016;28(10):2156–2177. doi: 10.1108/IJCHM-03-2015-0107

22. Lee PJ, Hu YH, Lu KT. Assessing the helpfulness of online hotel reviews: A classification-based approach. Telematics and Informatics. 2018;35(2):436–445. https://doi.org/10.1016/j.tele.2018.01.001.

23. Zeng YC, Ku T, Wu SH, Chen LP, Chen GD. Modeling the helpful opinion mining of online consumer reviews as a classification problem. International Journal of Computational Linguistics & Chinese Language Processing, Volume 19, Number 2, June 2014. 2014;19(2).

24. Akbarabadi M, Hosseini M. Predicting the helpfulness of online customer reviews: The role of title features. International Journal of Market Research. 2018; p. 1470785318819979. doi: 10.1177/1470785318819979

25. Vo C, Duong D, Nguyen D, Cao T. From Helpfulness Prediction to Helpful Review Retrieval for Online Product Reviews. In: Proceedings of the Ninth International Symposium on Information and Communication Technology. ACM; 2018. p. 38–45.

26. Haque ME, Tozal ME, Islam A. Helpfulness Prediction of Online Product Reviews. In: Proceedings of the ACM Symposium on Document Engineering 2018. DocEng’18. New York, NY, USA: ACM; 2018. p. 35:1–35:4. Available from: http://doi.acm.org/10.1145/3209280.3229105.

27. Chen J, Zhang C, Niu Z. Identifying Helpful Online Reviews with Word Embedding Features. In: Lehner F, Fteimi N, editors. Knowledge Science, Engineering and Management. Cham: Springer International Publishing; 2016. p. 123–133.

28. Fan M, Feng C, Guo L, Sun M, Li P. Product-Aware Helpfulness Prediction of Online Reviews. In: The World Wide Web Conference. WWW’19. ACM. New York, NY, USA: ACM; 2019. p. 2715–2721. Available from: http://doi.acm.org/10.1145/3308558.3313523.

29. Saumya S, Singh JP, Baabdullah AM, Rana NP, Dwivedi YK. Ranking online consumer reviews. Electronic Commerce Research and Applications. 2018;29:78–89. https://doi.org/10.1016/j.elerap.2018.03.008.

30. Hoffait AS, Ittoo A, Schyns M. Assessing and predicting review helpfulness: Critical review, open challenges and research agenda. In: 29ème conférence européenne sur la recherche opérationnelle (EURO2018); 2018.

31. Charrada EB. Which One to Read? Factors Influencing the Usefulness of Online Reviews for RE. In: 2016 IEEE 24th International Requirements Engineering Conference Workshops (REW); 2016. p. 46–52.

32. Zhang Z, Varadarajan B. Utility Scoring of Product Reviews. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management. CIKM’06. New York, NY, USA: ACM; 2006. p. 51–57. Available from: http://doi.acm.org/10.1145/1183614.1183626.

33. Liu H, Gao Y, Lv P, Li M, Geng S, Li M, et al. Using Argument-based Features to Predict and Analyse Review Helpfulness. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Copenhagen, Denmark: Association for Computational Linguistics; 2017. p. 1358–1363. Available from: https://www.aclweb.org/anthology/D17-1142.

34. Ngo-Ye TL, Sinha AP. The influence of reviewer engagement characteristics on online review helpfulness: A text regression model. Decision Support Systems. 2014;61:47–58. https://doi.org/10.1016/j.dss.2014.01.011.

35. Ngo-Ye TL, Sinha AP, Sen A. Predicting the helpfulness of online reviews using a scripts-enriched text regression model. Expert Systems with Applications. 2017;71:98–110. https://doi.org/10.1016/j.eswa.2016.11.029.

36. Ma Y, Xiang Z, Du Q, Fan W. Effects of user-provided photos on hotel review helpfulness: An analytical approach with deep leaning. International Journal of Hospitality Management. 2018;71:120–131. https://doi.org/10.1016/j.ijhm.2017.12.008.

37. Hwang SY, Lai CY, Jiang JJ, Chang S. The identification of Noteworthy Hotel Reviews for Hotel Management. In: Pacific Asia Journal of the Association for Information Systems (PACIS); 2014. p. 1–17.

38. Zhang Z, Ma Y, Chen G, Wei Q. Extending associative classifier to detect helpful online reviews with uncertain classes. In: Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology; 2015. p. 1134–1139.

39. Zheng X, Zhu S, Lin Z. Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems. 2013;56:211–222. https://doi.org/10.1016/j.dss.2013.06.002.

40. Dong R, Schaal M, Smyth B. Topic Extraction from Online Reviews for Classification and Recommendation. In: Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 13); 2013. p. 1310–1316.

41. Liu Y, Jiang C, Ding Y, Wang Z, Lv X, Wang J. Identifying helpful quality-related reviews from social media based on attractive quality theory. Total Quality Management & Business Excellence. 2017;0(0):1–20.

42. Xiong W, Litman D. Automatically Predicting Peer-review Helpfulness. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: Short Papers—Volume 2. HLT’11. Stroudsburg, PA, USA: Association for Computational Linguistics; 2011. p. 502–507. Available from: http://dl.acm.org/citation.cfm?id=2002736.2002836.

43. Maroun LB, Moro MM, Almeida JM, Silva APC. Assessing Review Recommendation Techniques Under a Ranking Perspective. In: Proceedings of the 27th ACM Conference on Hypertext and Social Media. HT’16. New York, NY, USA: ACM; 2016. p. 113–123. Available from: http://doi.acm.org/10.1145/2914586.2914598.

44. Mertz M, Korfiatis N, Zicari RV. Using Dependency Bigrams and Discourse Connectives for Predicting the Helpfulness of Online Reviews. In: Hepp M, Hoffner Y, editors. E-Commerce and Web Technologies. Cham: Springer International Publishing; 2014. p. 146–152.

45. Cao Q, Duan W, Gan Q. Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems. 2011;50(2):511–521. https://doi.org/10.1016/j.dss.2010.11.009.

46. Moghaddam S, Jamali M, Ester M. ETF: Extended Tensor Factorization Model for Personalizing Prediction of Review Helpfulness. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining. WSDM’12. New York, NY, USA: ACM; 2012. p. 163–172. Available from: http://doi.acm.org/10.1145/2124295.2124316.

47. Mukherjee S, Popat K, Weikum G. Exploring Latent Semantic Factors to Find Useful Product Reviews. In: Proceedings of the 2017 SIAM International Conference on Data Mining; 2017. p. 480–488. Available from: https://epubs.siam.org/doi/abs/10.1137/1.9781611974973.54.

48. Xiang Z, Du Q, Ma Y, Fan W. A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management. 2017;58:51–65. https://doi.org/10.1016/j.tourman.2016.10.001.

49. Yin D, Bond SD, Zhang H. Anxious or Angry? Effects of Discrete Emotions on the Perceived Helpfulness of Online Reviews. MIS Q. 2014;38(2):539–560. doi: 10.25300/MISQ/2014/38.2.10

50. Yin D, Mitra S, Zhang H. Research Note—When Do Consumers Value Positive vs. Negative Reviews? An Empirical Investigation of Confirmation Bias in Online Word of Mouth. Information Systems Research. 2016;27(1):131–144. doi: 10.1287/isre.2015.0617

51. Agnihotri A, Bhattacharya S. Online Review Helpfulness: Role of Qualitative Factors. Psychology & Marketing. 2016;33(11):1006–1017. doi: 10.1002/mar.20934

52. Chua AYK, Banerjee S. Helpfulness of user-generated reviews as a function of review sentiment, product type and information quality. Computers in Human Behavior. 2016;54:547–554. https://doi.org/10.1016/j.chb.2015.08.057.

53. Jing L, Xin X, Ngai E. An examination of the joint impacts of review content and reviewer characteristics on review usefulness-the case of Yelp. com. In: 22nd Americas Conference on Information Systems (AMCIS); 2016. p. 1–5.

54. Li ST, Pham TT, Chuang HC. Do reviewers’ words affect predicting their helpfulness ratings? Locating helpful reviewers by linguistics styles. Information & Management. 2019;56(1):28–38. https://doi.org/10.1016/j.im.2018.06.002.

55. Park YJ. Predicting the Helpfulness of Online Customer Reviews across Different Product Types. Sustainability. 2018;10(6).

56. Siering M, Muntermann J, Rajagopalan B. Explaining and predicting online review helpfulness: The role of content and reviewer-related signals. Decision Support Systems. 2018;108:1–12. https://doi.org/10.1016/j.dss.2018.01.004.

57. Liu J, Cao Y, Lin CY, Huang Y, Zhou M. Low-Quality Product Review Detection in Opinion Summarization. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL). Prague, Czech Republic: Association for Computational Linguistics; 2007. p. 334–342. Available from: http://www.aclweb.org/anthology/D/D07/D07-1035.

58. Liu Y, Jin J, Ji P, Harding JA, Fung RYK. Identifying helpful online reviews: A product designer’s perspective. Computer-Aided Design. 2013;45(2):180–194. https://doi.org/10.1016/j.cad.2012.07.008.

59. Singh JP, Irani S, Rana NP, Dwivedi YK, Saumya S, Roy PK. Predicting the “helpfulness” of online consumer reviews. Journal of Business Research. 2017;70:346–355. https://doi.org/10.1016/j.jbusres.2016.08.008.

60. Baek H, Ahn J, Choi Y. Helpfulness of Online Consumer Reviews: Readers’ Objectives and Review Cues. International Journal of Electronic Commerce. 2012;17(2):99–126. doi: 10.2753/JEC1086-4415170204

61. Baek H, Lee S, Oh S, Ahn JH. Normative social influence and online review helpfulness: Polynomial modeling and response surface analysis. Journal of Electronic Commerce Research. 2015;16:290–306.

62. Lee M, Jeong M, Lee J. Roles of negative emotions in customers’ perceived helpfulness of hotel reviews on a user-generated review website: A text mining approach. International Journal of Contemporary Hospitality Management. 2017;29(2):762–783. doi: 10.1108/IJCHM-10-2015-0626

63. Salehan M, Kim DJ. Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems. 2016;81:30–40. https://doi.org/10.1016/j.dss.2015.10.006.

64. Mousavizadeh M, Koohikamali M, Salehan M. The Effect of Central and Peripheral Cues on Online Review Helpfulness: A Comparison between Functional and Expressive Products. In: Thirty Sixth International Conference on Information Systems, Fort Worth (ICIS); 2015. p. 1–22.

65. Ghose A, Ipeirotis PG. Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics. IEEE Transactions on Knowledge and Data Engineering. 2011;23(10):1498–1512. doi: 10.1109/TKDE.2010.188

66. Liu Z, Park S. What makes a useful online review? Implication for travel product websites. Tourism Management. 2015;47:140–151. https://doi.org/10.1016/j.tourman.2014.09.020.

67. Wu PF, Van Der Heijden H, Korfiatis N. The influences of negativity and review quality on the helpfulness of online reviews. In: International conference on information systems; 2011. p. 1–10.

68. Korfiatis N, García-Bariocanal E, Sánchez-Alonso S. Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications. 2012;11(3):205–217. https://doi.org/10.1016/j.elerap.2011.10.003.

69. Park S, Nicolau JL. Asymmetric effects of online consumer reviews. Annals of Tourism Research. 2015;50:67–83. https://doi.org/10.1016/j.annals.2014.10.007.

70. Wang Y, Wang J, Yao T. What makes a helpful online review? A meta-analysis of review characteristics. Electronic Commerce Research. 2018.

71. Lee S, Choeh JY. Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Systems with Applications. 2014;41(6):3041–3046. https://doi.org/10.1016/j.eswa.2013.10.034.

72. Bjering E, Havro LJ, Moen Ø. An empirical investigation of self-selection bias and factors influencing review helpfulness. International Journal of Business and Management. 2015;10(7):16.

73. Pan Y, Zhang JQ. Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews. Journal of Retailing. 2011;87(4):598–612. https://doi.org/10.1016/j.jretai.2011.05.002.

74. Einar B. Online Consumer Reviews: The Moderating Effect of Product Category. Norwegian University of Science and Technology; 2014.

75. Chevalier JA, Mayzlin D. The Effect of Word of Mouth on Sales: Online Book Reviews. Journal of Marketing Research. 2006;43(3):345–354. doi: 10.1509/jmkr.43.3.345

76. Mudambi SM, Schuff D. Research Note: What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon.com. MIS Quarterly. 2010;34(1):185–200. doi: 10.2307/20721420

77. Huang AH, Chen K, Yen DC, Tran TP. A study of factors that contribute to online review helpfulness. Computers in Human Behavior. 2015;48:17–27. https://doi.org/10.1016/j.chb.2015.01.010.

78. O’Mahony MP, Smyth B. A Classification-based Review Recommender. In: Bramer M, Ellis R, Petridis M, editors. Research and Development in Intelligent Systems XXVI. London: Springer London; 2010. p. 49–62.

79. Otterbacher J.’Helpfulness’ in Online Communities: A Measure of Message Quality. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI’09. New York, NY, USA: ACM; 2009. p. 955–964. Available from: http://doi.acm.org/10.1145/1518701.1518848.

80. Huang AH, Yen DC. Predicting the Helpfulness of Online Reviews—A Replication. International Journal of Human–Computer Interaction. 2013;29(2):129–138. doi: 10.1080/10447318.2012.694791

81. Wu J. Review popularity and review helpfulness: A model for user review effectiveness. Decision Support Systems. 2017;97:92–103. https://doi.org/10.1016/j.dss.2017.03.008.

82. Lee S, Choeh JY. Exploring the determinants of and predicting the helpfulness of online user reviews using decision trees. Management Decision. 2017;55(4):681–700. doi: 10.1108/MD-06-2016-0398

83. Zhao P, Wu J, Hua Z, Fang S. Finding eWOM customers from customer reviews. Industrial Management & Data Systems. 0;0(0):null.

84. Hong Y, Lu J, Yao J, Zhu Q, Zhou G. What Reviews Are Satisfactory: Novel Features for Automatic Helpfulness Voting. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. SIGIR’12. New York, NY, USA: ACM; 2012. p. 495–504. Available from: http://doi.acm.org/10.1145/2348283.2348351.

85. Qazi A, Syed KBS, Raj RG, Cambria E, Tahir M, Alghazzawi D. A concept-level approach to the analysis of online review helpfulness. Computers in Human Behavior. 2016;58:75–81. https://doi.org/10.1016/j.chb.2015.12.028.

86. Salton G, Buckley C. Term-weighting Approaches in Automatic Text Retrieval. Inf Process Manage. 1988;24(5):513–523. doi: 10.1016/0306-4573(88)90021-0

87. Blei DM, Ng AY, Jordan MI. Latent dirichlet allocation. Journal of machine Learning research. 2003;3(Jan):993–1022.

88. Yang Y, Chen C, Bao FS. Aspect-Based Helpfulness Prediction for Online Product Reviews. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI); 2016. p. 836–843.

89. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems; 2013. p. 3111–3119.

90. Pennington J, Socher R, Manning CD. GloVe: Global Vectors for Word Representation. In: Empirical Methods in Natural Language Processing (EMNLP); 2014. p. 1532–1543. Available from: http://www.aclweb.org/anthology/D14-1162.

91. Chatterjee S. Drivers of helpfulness of online hotel reviews: A sentiment and emotion mining approach. International Journal of Hospitality Management. 2019; p. 102356. https://doi.org/10.1016/j.ijhm.2019.102356.

92. Pennebaker JW, Boyd RL, Jordan K, Blackburn K. The development and psychometric properties of LIWC2015. The University of Texas at Austin; 2015.

93. J SP, F BR, Zvi NJ, M OD. The general inquirer: A computer system for content analysis and retrieval based on the sentence as a unit of information. Behavioral Science. 1966;7(4):484–498.

94. Scherer KR. What are emotions? And how can they be measured? Social Science Information. 2005;44(4):695–729. doi: 10.1177/0539018405058216

95. Hu M, Liu B. Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM; 2004. p. 168–177.

96. Baccianella S, Esuli A, Sebastiani F. SentiWordNet 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining. In: Proceedings of LREC. vol. 10; 2010.

97. Fellbaum C. WordNet: An electronic lexical database (Language, Speech, and Communication). The MIT Press; 1998.

98. Thelwall M, Buckley K, Paltoglou G. Sentiment Strength Detection for the Social Web. J Am Soc Inf Sci Technol. 2012;63(1):163–173. doi: 10.1002/asi.21662

99. Hutto CJ, Gilbert E. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text. In: International AAAI Conference on Web and Social Media; 2014. p. 216–225. Available from: https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109.

100. Ribeiro FN, Araújo M, Gonçalves P, André Gonçalves M, Benevenuto F. SentiBench—a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Science. 2016;5(1):23. doi: 10.1140/epjds/s13688-016-0085-1

101. Yue L, Chen W, Li X, Zuo W, Yin M. A survey of sentiment analysis in social media. Knowledge and Information Systems. 2019;60(2):617–663. doi: 10.1007/s10115-018-1236-4

102. DuBay WH. Smart Language: Readers, Readability, and the Grading of Text. ERIC; 2007.

103. Farr JN, Jenkins JJ, Paterson DG. Simplification of Flesch Reading Ease Formula. Journal of applied psychology. 1951;35(5):333. doi: 10.1037/h0062427

104. Kincaid JP, Aagard JA, O’Hara JW, Cottrell LK. Computer readability editing system. IEEE Transactions on Professional Communication. 1981;PC-24(1):38–42. doi: 10.1109/TPC.1981.6447821

105. Gunning R. The Fog Index After Twenty Years. Journal of Business Communication. 1969;6(2):3–13. doi: 10.1177/002194366900600202

106. Mc Laughlin GH. SMOG grading–a new readability formula. Journal of reading. 1969;12(8):639–646.

107. Smith EA, Kincaid JP. Derivation and Validation of the Automated Readability Index for Use with Technical Materials. Human Factors. 1970;12(5):457–564. doi: 10.1177/001872087001200505

108. Coleman M, Liau TL. A computer readability formula designed for machine scoring. Journal of Applied Psychology. 1975;60(2):283. doi: 10.1037/h0076540

109. Benjamin RG. Reconstructing Readability: Recent Developments and Recommendations in the Analysis of Text Difficulty. Educational Psychology Review. 2012;24(1):63–88. doi: 10.1007/s10648-011-9181-8

110. Xiong W, Litman D. Empirical analysis of exploiting review helpfulness for extractive summarization of online reviews. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers. Dublin, Ireland: Dublin City University and Association for Computational Linguistics; 2014. p. 1985–1995. Available from: http://www.aclweb.org/anthology/C14-1187.

111. Jindal N, Liu B. Mining Comparative Sentences and Relations. In: Proceedings of the 21st National Conference on Artificial Intelligence—Volume 2. AAAI’06. AAAI Press; 2006. p. 1331–1336. Available from: http://dl.acm.org/citation.cfm?id=1597348.1597400.

112. He R, McAuley J. Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering. In: Proceedings of the 25th International Conference on World Wide Web. WWW’16. Republic and Canton of Geneva, Switzerland: International World Wide Web Conferences Steering Committee; 2016. p. 507–517. Available from: https://doi.org/10.1145/2872427.2883037.

113. Danescu-Niculescu-Mizil C, Kossinets G, Kleinberg J, Lee L. How Opinions Are Received by Online Communities: A Case Study on Amazon.Com Helpfulness Votes. In: Proceedings of the 18th International Conference on World Wide Web. WWW’09. New York, NY, USA: ACM; 2009. p. 141–150. Available from: http://doi.acm.org/10.1145/1526709.1526729.

114. Roy G, Datta B, Mukherjee S. Role of electronic word-of-mouth content and valence in influencing online purchase behavior. Journal of Marketing Communications. 2018;0(0):1–24.

115. Bird S, Klein E, Loper E. Natural Language Processing with Python. 1st ed. O’Reilly Media, Inc.; 2009.

116. Vapnik VN. The Nature of Statistical Learning Theory. Berlin, Heidelberg: Springer-Verlag; 1995.

117. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–2830.

118. Wan Y. The Matthew effect in social commerce. Electronic Markets. 2015;25(4):313–324. doi: 10.1007/s12525-015-0186-x

119. Sipos R, Ghosh A, Joachims T. Was This Review Helpful to You?: It Depends! Context and Voting Patterns in Online Content. In: Proceedings of the 23rd International Conference on World Wide Web. WWW’14. New York, NY, USA: ACM; 2014. p. 337–348. Available from: http://doi.acm.org/10.1145/2566486.2567998.


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