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Sells Out or Piles Up? A Sentiment Autoregressive Model for Predicting Sales Performance

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Information Retrieval Technology (AIRS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7675))

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Abstract

The development of e-commerce has witnessed the explosion of online reviews which represent the voices of the public. These reviews are helpful for consumers in making purchasing decisions, and this effect can be observed by some easy-to-measure economic variables, such as sales performance or product prices. In this paper, we study the problem of mining sentiment information from reviews and investigate whether applying sentiment analysis methods can turn out better sales predictions. Based on the nature of various presentations of sentiments, we propose a Latent Sentiment Language (LSL) Model to address this challenge, in which sentiment-language model and sentiment-LDA are used to capture the explicit and implicit sentiment information respectively. Subsequently, we explore ways to use such information to predict product sales, and to generate an SAR, a sentiment autoregressive model. Extensive experiments indicate the predictive power of sentiment information, as well as the superior performance of the SAR model.

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References

  1. Ghose, A., Ipeirotis, P.G., Sundararajan, A.: Opinion Mining Using Econometrics: A Case Study on Reputation Systems. In: 45th Annual Meeting of the Association of Computational Linguistics, pp. 416–423. Association for Computational Linguistics, Stroudsburg (2007)

    Google Scholar 

  2. Kim, S.M., Hovy, E.: Crystal: Analyzing Predictive Opinions on the Web. In: 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1056–1064. Association for Computational Linguistics, Stroudsburg (2007)

    Google Scholar 

  3. Devitt, A., Ahmad, K.: Sentiment Polarity Identification in Financial News: A Cohesion-based Approach. In: 45th Annual Meeting of the Association of Computational Linguistics, pp. 984–991. Association for Computational Linguistics, Stroudsburg (2007)

    Google Scholar 

  4. Mishne, G., Glance, N.: Predicting Movie Sales from Blogger Sentiment. In: 2006 AAAI Symposium on Computational Approaches to Analysing Weblogs, pp. 155–158. American Association for Artificial Intelligence, California (2006)

    Google Scholar 

  5. Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins, A.: The Predictive Power of Online Chatter. In: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 78–87. ACM, New York (2005)

    Chapter  Google Scholar 

  6. Liu, Y., Huang, X.J., An, A.J., Yu, X.H.: ARSA: A Sentiment-Aware Model for Predicting Sales Performance Using Blogs. In: 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 607–614. ACM, New York (2007)

    Chapter  Google Scholar 

  7. Box, G.E.P., Jenkins, G.M., Reinsel, G.C.: Time Series Analysis. Holden-Day, San Francisco (1976)

    MATH  Google Scholar 

  8. Enders, W.: Applied Econometric Time Series. John Wiley and Sons, New York (2004)

    Google Scholar 

  9. Wiebe, J.: Learning Subjective Adjectives from Corpora. In: 17th National Conference on Artificial Intelligence and 12th Conference on Innovative Applications of Artificial Intelligence, pp. 735–740. AAAI Press/The MIT Press, Cambridge, MA (2000)

    Google Scholar 

  10. Hatzivassiloglou, V., Wiebe, J.M.: Effects of Adjective Orientation and Gradability on Sentence Subjectivity. In: 18th Conference on Computational Linguistics, pp. 174–181. Association for Computational Linguistics, Stroudsburg (2000)

    Google Scholar 

  11. Turney, P.D.: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews. In: 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424. Association for Computational Linguistics, Stroudsburg (2002)

    Google Scholar 

  12. Kamps, J., Marx, M., Mokken, R.J., Rijke, M.D.: Using WordNet to Measure Semantic Orientation of Adjectives. In: 4th International Conference on Language Resources and Evaluation, Lisbon, PT, pp. 1115–1118 (2004)

    Google Scholar 

  13. Yu, H., Hatzivassiloglou, V.: Towards Answering Opinion Questions: Separating Facts From Opinions and Identifying the Polarity of Opinion Sentences. In: 2003 Conference on Empirical Methods in Natural Language Processing, pp. 129–136. Association for Computational Linguistics, Stroudsburg (2003)

    Chapter  Google Scholar 

  14. Xu, L.H., Lin, H.F., Pan, Y., Ren, H., Chen, J.M.: Constructing the Affective Lexicon Ontology. Journal of the China Society for Scientific and Technical Information 2, 180–185 (2008)

    Google Scholar 

  15. Hu, M.Q., Liu, B.: Mining and Summarizing Customer Reviews. In: 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 168–177. ACM, New York (2004)

    Google Scholar 

  16. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up? Sentiment Classification Using Machine Learning Techniques. In: 2002 Conference on Empirical Methods in Natural Language Processing, pp. 79–86. Association for Computational Linguistics, Stroudsburg (2002)

    Chapter  Google Scholar 

  17. Pang, B., Lee, L.: Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales. In: 43rd Annual Meeting of the Association for Computational Linguistics, pp. 115–124. Association for Computational Linguistics, Stroudsburg (2005)

    Google Scholar 

  18. Zhang, Z., Varadarajan, B.: Utility Scoring of Product Reviews. In: 15th ACM International Conference on Information and Knowledge Management, pp. 51–57. ACM, New York (2006)

    Google Scholar 

  19. Popescu, A.M., Etzioni, O.: Extracting Product Features and Opinions from Reviews. In: 2005 Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 339–346. Association for Computational Linguistics, Stroudsburg (2005)

    Chapter  Google Scholar 

  20. Liu, B., Hu, M., Cheng, J.S.: Opinion Observer: Analyzing and Comparing Opinions on the Web. In: 14th International Conference on World Wide Web, pp. 342–351. ACM, New York (2005)

    Chapter  Google Scholar 

  21. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993–1022 (2003)

    MATH  Google Scholar 

  22. Croft, W.B., Metzler, D., Strohman, T.: Search Engines: Information Retrieval in Practice. Addison-Wesley Publishing Company, Boston (2009)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Information Retrieval Laboratory, School of Computer Science and Technology, Dalian University of Technology, Dalian, China, 116024

    Xueni Li, Shaowu Zhang, Liang Yang & Hongfei Lin

Authors
  1. Xueni Li
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  2. Shaowu Zhang
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  3. Liang Yang
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  4. Hongfei Lin
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Editor information

Editors and Affiliations

  1. School of computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Yuexian Hou

  2. DIRO, University of Montreal, CP. 6128, succursale Centre-ville, H3C 3J7, Montreal, QC, Canada

    Jian-Yun Nie

  3. Institute of Software, Storage & Information Retrieval Laboratory, Chinese Academy of Sciences, 100190, Beijing, China

    Le Sun

  4. School of Computer Science and Technology, Tianjin University, 300072, Tianjin, China

    Bo Wang

  5. School of Computing, Robert Gordon University, St Andrew Street, AB25 1HG, Aberdeen, UK

    Peng Zhang

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© 2012 Springer-Verlag Berlin Heidelberg

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Li, X., Zhang, S., Yang, L., Lin, H. (2012). Sells Out or Piles Up? A Sentiment Autoregressive Model for Predicting Sales Performance. In: Hou, Y., Nie, JY., Sun, L., Wang, B., Zhang, P. (eds) Information Retrieval Technology. AIRS 2012. Lecture Notes in Computer Science, vol 7675. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35341-3_8

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  • DOI: https://doi.org/10.1007/978-3-642-35341-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35340-6

  • Online ISBN: 978-3-642-35341-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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