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Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis

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Book cover Advances in Social Media Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 602))

Abstract

Sentiment analysis aims to automatically estimate the sentiment in a given text as positive, objective or negative, possibly together with the strength of the sentiment. Polarity lexicons that indicate how positive or negative each term is, are often used as the basis of many sentiment analysis approaches. Domain-specific polarity lexicons are expensive and time-consuming to build; hence, researchers often use a general purpose or domain-independent lexicon as the basis of their analysis. In this work, we address two sub-tasks in sentiment analysis. We apply a simple method to adapt a general purpose polarity lexicon to a specific domain [1]. Subsequently, we propose and evaluate new features to be used in a word polarity based approach to sentiment classification. In particular, we analyze sentences as the first step for estimating the overall review polarity. We consider different aspects of sentences, such as length, purity, irrealis content, subjectivity, and position within the opinionated text. This analysis is then used to find sentences that may convey better information about the overall review polarity. We use a subset of hotel reviews from the TripAdvisor database [2] to evaluate the effect of sentence-level features on sentiment classification. Then, we measure the performance of our sentiment analysis engine using the domain-adapted lexicon on a large subset of the TripAdvisor database.

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References

  1. Demiroz, G., Yanikoglu, B. Tapucu, D., Saygin, Y.: Learning domain-specific polarity lexicons, In: 2012 IEEE 12th International Conference on Data Mining Workshops (ICDMW), pp. 674–679 (2012)

    Google Scholar 

  2. The TripAdvisor website. http://www.tripadvisor.com [TripAdvisor LLC]. Accessed in 2012

  3. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)

    Article  Google Scholar 

  4. Turney, P.D.: 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, pp. 417–424. Association for Computational Linguistics (2002)

    Google Scholar 

  5. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 conference on Empirical methods in natural language processing, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)

    Google Scholar 

  6. Esuli, A., Sebastiani, F.: SentiWordNet: a publicly available lexical resource for opinion mining. In: Proceedings of the 5th Conference on Language Resources and Evaluation (LREC06), pp. 417–422 (2006)

    Google Scholar 

  7. Taboada, M., Brooke, J., Tofiloski, M., Voll, K.D., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)

    Article  Google Scholar 

  8. Zhao, J., Liu, K., Wang, G.: Adding redundant features for crfs-based sentence sentiment classification. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 117–126 (2008)

    Google Scholar 

  9. Poria, S., Gelbukh, A.F., Cambria, E., Das, D., Bandyopadhyay, S.: Enriching SenticNet polarity scores through semi-supervised fuzzy clustering. In: Vreeken, J., Ling, C., Zaki, M.J., Siebes, A., Yu, J.X., Goethals, B., Webb, G.I., Wu, X. (eds.) ICDM Workshops, pp. 709–716. IEEE Computer Society (2012)

    Google Scholar 

  10. Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 conference on Empirical methods in Natural Language Processing, pp. 129–136. Association for Computational Linguistics (2003)

    Google Scholar 

  11. Bespalov, D., Bai, B., Qi, Y., Shokoufandeh, A.: Sentiment classification based on supervised latent n-gram analysis. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 375–382. ACM (2011)

    Google Scholar 

  12. Bespalov, D., Qi, Y., Bai, B., Shokoufandeh, A.: Sentiment lassification with supervised sequence embedding. In: Machine Learning and Knowledge Discovery in Databases, pp. 159–174. Springer (2012)

    Google Scholar 

  13. Hatzivassiloglou, V., Mckeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of ACL-97, 35th Annual Meeting of the Association for Computational Linguistics, pp. 174–181. Association for Computational Linguistics (1997)

    Google Scholar 

  14. Mao, Y., Lebanon, G.: Isotonic conditional random fields and local sentiment flow. Adv. Neural Inf. Process. Syst. 19, 961 (2007)

    Google Scholar 

  15. Pang, B., Lee, L.: A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on Association for Computational Linguistics, p. 271. Association for Computational Linguistics (2004)

    Google Scholar 

  16. Wiebe, J.M.: Learning subjective adjectives from corpora. In: In AAAI, pp. 735–740 (2000)

    Google Scholar 

  17. Hatzivassiloglou, V., Wiebe, J.: Effects of adjective orientation and gradability on sentence subjectivity. In: Proceedings of the 18th Conference on Computational Linguistics, vol. 2, pp. 299–305. Universität des Saarlandes, Saarbrücken, Germany, July 31–Aug 4 (2000)

    Google Scholar 

  18. Wiebe, J., Mihalcea, R.: Word sense and subjectivity. In: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pp. 1065–1072. Association for Computational Linguistics (2006)

    Google Scholar 

  19. Wiebe, J., Wilson, T., Bruce, R., Bell, M., Martin, M.: Learning subjective language. Comput. Linguist. 30(3), 277–308 (2004)

    Article  Google Scholar 

  20. Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Mining Text Data, pp. 415–463. Springer (2012)

    Google Scholar 

  21. Das, S.R., Chen, M.Y.: Yahoo! for amazon: sentiment extraction from small talk on the web. Manage. Sci. 53(9), 1375–1388 (2007)

    Article  Google Scholar 

  22. Turney, P.D., Littman, M.L.: Measuring praise and criticism: inference of semantic orientation from association. ACM Trans. Inf. Syst. (TOIS) 21(4), 315–346 (2003)

    Article  Google Scholar 

  23. Miller, G.A.: Wordnet: a lexical database for english. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  24. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009)

    Article  Google Scholar 

  25. Qiu, G., Liu, B., Bu, J., Chen, C.: Expanding domain sentiment lexicon through double propagation. In: Proceedings of the 21st international jont conference on Artifical intelligence, pp. 1199–1204 (2009)

    Google Scholar 

  26. Choi, Y., Cardie, C.: Adapting a polarity lexicon using integer linear programming for domainspecific sentiment classification. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp. 590–598 (2009)

    Google Scholar 

  27. Dragut, E.C., Yu, C., Sistla, P., Meng, W.: Construction of a sentimental word dictionary. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, pp. 1761–1764. ACM, New York, NY, USA (2010)

    Google Scholar 

  28. Lu, Y., Castellanos, M., Dayal, U., Zhai, C.: Automatic construction of a context-aware sentiment lexicon: an optimization approach. In: Proceedings of the 20th International Conference on World Wide Web, WWW ’11, pp. 347–356. ACM, New York, NY, USA (2011)

    Google Scholar 

  29. Paltoglou, G., Gobron, S., Skowron, M., Thelwall, M., Thalmann, D.: Sentiment analysis of informal textual communication in cyberspace. Proc. Engage 13–25 (2010)

    Google Scholar 

  30. McDonald, R., Hannan, K., Neylon, T., Wells, M., Reynar, J.: Structured models for fine-to-coarse sentiment analysis. In: Annual Meeting-Association For Computational Linguistics, vol. 45, p. 432 (2007)

    Google Scholar 

  31. Kim, S.-M., Hovy, E.: Automatic detection of opinion bearing words and sentences. In: Proceedings of IJCNLP, vol. 5 (2005)

    Google Scholar 

  32. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. Association for Computational Linguistics (2005)

    Google Scholar 

  33. Meena, A., Prabhakar, T.: Sentence level sentiment analysis in the presence of conjuncts using linguistic analysis. In: Advances in Information Retrieval, pp. 573–580. Springer (2007)

    Google Scholar 

  34. Martineau, J., Finin, T.: Delta tfidf: an improved feature space for sentiment analysis. In: ICWSM (2009)

    Google Scholar 

  35. Salton, G., Wong, A., Yang, C.-S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  36. Denecke, K.: How to assess customer opinions beyond language barriers? In: ICDIM, IEEE, pp. 430–435 (2008)

    Google Scholar 

  37. Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Discovery Science, pp. 1–15. Springer (2010)

    Google Scholar 

  38. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: LREC (2010)

    Google Scholar 

  39. Zhang, E., Zhang, Y.: Ucsc on trec 2006 blog opinion mining. In: Text Retrieval Conference (2006)

    Google Scholar 

  40. Chang, C.-C., Lin, C.-J.: Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)

    Google Scholar 

  41. Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783–792 (2010)

    Google Scholar 

  42. Bespalov, D., Qi, Y., Bai, B., Shokoufandeh, A.: Sentiment classification with supervised sequence embedding. In: Flach,P.A. Bie, T.D., Cristianini, N. (eds.) ECML/PKDD (1). Lecture Notes in Computer Science, vol. 7523, pp. 159–174. Springer (2012)

    Google Scholar 

  43. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The weka data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  44. Esuli, A., Sebastiani, F.: Determining term subjectivity and term orientation for opinion mining. In: Proceedings of EACL, vol. 6, pp. 193–200 (2006)

    Google Scholar 

  45. Lau, R.Y.K., Lai, C.L., Bruza, P.B., Wong, K.F.: Leveraging web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM ’11, pp. 2457–2460. ACM, New York, NY, USA (2011)

    Google Scholar 

  46. Gindl, S., Weichselbraun, A., Scharl, A.: Cross-domain contextualisation of sentiment lexicons. In: Proceedings of the 19th European Conference on Artificial Intelligence (ECAI), 16 Aug 2010

    Google Scholar 

  47. Gezici, G., Yanikoglu, B., Tapucu, D., Saygın, Y.: New features for sentiment analysis: Do sentences matter?. In: SDAD 2012 The 1st International Workshop on Sentiment Discovery from Affective Data, p. 5 (2012)

    Google Scholar 

  48. Gräbner, D., Zanker, M., Fliedl, G., Fuchs, M.: Classification of customer reviews based on sentiment analysis. In: Information and Communication Technologies in Tourism 2012, pp. 460–470. Springer (2012)

    Google Scholar 

  49. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Lin, D., Matsumoto, Y., Mihalcea, R. (eds.) ACL, pp. 142–150. The Association for Computer Linguistics (2011)

    Google Scholar 

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Acknowledgments

This work was partially funded by European Commission, FP7, under UBIPOL (Ubiquitous Participation Platform for Policy Making) Project (www.ubipol.eu). Dr. Dilek Tapucu was a post-doctoral researcher at Sabanci University at the time of this project.

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Authors and Affiliations

  1. Faculty of Engineering and Natural Sciences, Sabanci University, 34956, Istanbul, Turkey

    Gizem Gezici, Berrin Yanikoglu & Yücel Saygın

  2. Department of Computer Engineering, Izmir Institute of Technology, 35430, Izmir, Turkey

    Dilek Tapucu

Authors
  1. Gizem Gezici
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  2. Berrin Yanikoglu
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  3. Dilek Tapucu
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  4. Yücel Saygın
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Correspondence to Berrin Yanikoglu .

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Editors and Affiliations

  1. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Mohamed Medhat Gaber

  2. School of Computing, University of Portsmouth, Portsmouth, United Kingdom

    Mihaela Cocea

  3. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Nirmalie Wiratunga

  4. School of Computing Science and Digital Media, Robert Gordon University, Aberdeen, United Kingdom

    Ayse Goker

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Gezici, G., Yanikoglu, B., Tapucu, D., Saygın, Y. (2015). Sentiment Analysis Using Domain-Adaptation and Sentence-Based Analysis. In: Gaber, M., Cocea, M., Wiratunga, N., Goker, A. (eds) Advances in Social Media Analysis. Studies in Computational Intelligence, vol 602. Springer, Cham. https://doi.org/10.1007/978-3-319-18458-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-18458-6_3

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