DOI QR코드

DOI QR Code

Multidimensional Analysis of Consumers' Opinions from Online Product Reviews

  • Taewook Kim (Philosophy & Computer Science, Hong Kong University of Science and Technology) ;
  • Dong Sung Kim (Business Administration at the School of Business, Hanyang University) ;
  • Donghyun Kim (LG Electronics Company) ;
  • Jong Woo Kim (School of Business, Hanyang University)
  • Received : 2019.04.30
  • Accepted : 2019.09.25
  • Published : 2019.12.31

Abstract

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

Keywords

Acknowledgement

This work was supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) [2016-0-00562(R0124-16-0002), Emotional Intelligence Technology to Infer Human Emotion and Carry on Dialogue Accordingly].

References

  1. Adams, J. L. (2012). Good products, bad products: Essential elements to achieving superior quality. McGraw-Hill. 
  2. Agarwal, A., Sharma, V., Sikka, G., and Dhir, R. (2016). Opinion mining of news headlines using SentiWordNet. In Symposium on Colossal Data Analysis and Networking, IEEE, 1-5. 
  3. Baccianella, S., Esuli, A., and Sebastiani, F. (2010). Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC'10), 2200-2204. 
  4. Day, G. S. (1994). The capabilities of market-driven organizations. Journal of marketing, 58(4), 37-52.  https://doi.org/10.1177/002224299405800404
  5. Duan, W., Cao, Q., Yu, Y., and Levy, S. (2013). Mining online user-generated content: using sentiment analysis technique to study hotel service quality. In 46th Hawaii International Conference on System Sciences, IEEE, 3119-3128. 
  6. Fellbaum, C. (2005). WordNet and wordnets. In: Brown, Keith et al. (eds.), Encyclopedia of Language and Linguistics, Second Edition, Oxford: Elsevier, 665-670. 
  7. Garten, J., Sagae, K., Ustun, V., & Dehghani, M. (2015). Combining distributed vector representations for words. In Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing, 95-101. 
  8. Giachanou, A., and Crestani, F. (2016). Opinion retrieval in Twitter: Is proximity effective? In Proceedings of the 31st Annual ACM Symposium on Applied Computing, ACM, 1146-1151. 
  9. Hu, M., and Liu, B. (2004). Mining and summarizing customer reviews. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, 168-177. 
  10. Hu, N., Pavlou, P. A., and Zhang, J. (2006). Can online reviews reveal a product's true quality?: Empirical findings and analytical modeling of online word-of-mouth communication. In Proceedings of the 7th ACM conference on Electronic commerce, ACM, 324-330. 
  11. Hutto, C. J., and Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. In Eighth international AAAI conference on weblogs and social media, 216-225. 
  12. Iosifidis, V., and Ntoutsi, E. (2017). Large scale sentiment learning with limited labels. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, 1823-1832. 
  13. Jolliffe, I. T. (2002). Principal components in regression analysis. Principal Component analysis, 167-198. 
  14. Kim, J. B., and Shin, S. I. (2015). An empirical study on the interaction effects between the customer reviews and the customer incentives towards the product sales at the online retail store. Asia Pacific Journal of Information Systems, 25(4), 763-783.  https://doi.org/10.14329/apjis.2015.25.4.763
  15. Kim, Y., Moon, H. S., and Kim, J. K. (2017) Analyzing the effect of electronic word of mouth on low involvement products. Asia Pacific Journal of Information Systems, 27(3), 139-155.  https://doi.org/10.14329/apjis.2017.27.3.139
  16. Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882. 
  17. Koo, C., Shin, S., Hlee, S., Moon, D., and Chung, N. (2015) Online tourism review: Three phases for successful destination relationships. Asia Pacific Journal of Information Systems, 25(4), 746-762.  https://doi.org/10.14329/apjis.2015.25.4.746
  18. Kotler, P., and Levy, S. J. (1969). Broadening the concept of marketing. Journal of marketing, 33(1), 10-15.  https://doi.org/10.1177/002224296903300103
  19. Kotler, P., Keller, K. L., Ancarani, F., and Costabile, M. (2012). Marketing management 12/e. Pearson. 
  20. Kordupleski, R. (2003). Mastering customer value management: The art and science of creating competitive advantage. Customer Value Management I. 
  21. Lebret, R., and Collobert, R. (2013). Word emdeddings through hellinger PCA. arXiv preprint arXiv:1312.5542. 
  22. Lilleberg, J., Zhu, Y., and Zhang, Y. (2015). Support vector machines and word2vec for text classification with semantic features. In 2015 IEEE 14th International Conference on Cognitive Informatics & Cognitive Computing, 136-140. 
  23. Liu, B. (2010). Sentiment analysis and subjectivity. Handbook of natural language processing, 627-666. 
  24. Liu, B. (2012). Sentiment analysis and opinion mining. Morgan & Claypool Publishers. 
  25. Liu, B., Hu, M., and Cheng, J. (2005). Opinion observer: Analyzing and comparing opinions on the web. In Proceedings of the 14th international conference on World Wide Web, ACM, 342-351. 
  26. Lu, B., Ott, M., Cardie, C., and Tsou, B. K. (2011). Multi-aspect sentiment analysis with topic models. In 2011 IEEE 11th international conference on data mining workshops, IEEE, 81-88. 
  27. Ma, X., Fellbaum, C. (2012). Rethinking WordNet's domains. In Proceedings of Global WordNet Conference, 173-180. 
  28. McCarthy, E. J., Shapiro, S. J., and Perreault, W. D. (1979). Basic marketing. Irwin-Dorsey. 
  29. Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013a). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. 
  30. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, 3111-3119. 
  31. Nie, F., Yuan, J., and Huang, H. (2014). Optimal mean robust principal component analysis. In International conference on machine learning, ICML'14, 1062-1070. 
  32. Oram, P. (1998). Wordnet: An electronic lexical database. Christiane fellbaum. 
  33. Paul, D., Li, F., Teja, M. K., Yu, X., and Frost, R. (2017). Compass: Spatio temporal sentiment analysis of us election what twitter says!. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, ACM, 1585-1594. 
  34. Piercy, N. F. (2016). Market-led strategic change: Transforming the process of going to market. Routledge. 
  35. Swoboda, T., Hemmje, M., Dascalu, M., and Trausan-Matu, S. (2016). Combining taxonomies using word2vec. In Proceedings of the 2016 ACM Symposium on Document Engineering, 131-134. 
  36. Wang, Y., Aguirre-Urreta, M., and Song, J. (2016). Investigating the value of information in mobile commerce: A text mining approach. Asia Pacific Journal of Information System, 26(4), 577-592.  https://doi.org/10.14329/apjis.2016.26.4.577
  37. Zhai, Z., Liu, B., Xu, H., and Jia, P. (2011). Clustering product features for opinion mining. In Proceedings of the fourth ACM international conference on Web search and data mining, ACM, 347-354. 
  38. Zhang, Z., and Varadarajan, B. (2006, November). Utility scoring of product reviews. In Proceedings of the 15th ACM international conference on Information and knowledge management (pp. 51-57). ACM. 
  39. Zhu, F., and Zhang, X. (2010). Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. Journal of Marketing, 74(2), 133-148. https://doi.org/10.1509/jm.74.2.133