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Mining Semantic Patterns for Sentiment Analysis of Product Reviews

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Research and Advanced Technology for Digital Libraries (TPDL 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10450))

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Abstract

A central challenge in building sentiment classifiers using machine learning approach is the generation of discriminative features that allow sentiment to be implied. Researchers have made significant progress with various features such as n-grams, sentiment shifters, and lexicon features. However, the potential of semantics-based features in sentiment classification has not been fully explored. By integrating PropBank-based semantic parsing and class association rule (CAR) mining, this study aims to mine patterns of semantic labels from domain corpus for sentence-level sentiment analysis of product reviews. With the features generated from the semantic patterns, the F-score of the sentiment classifier was boosted to 82.31% at minimum confidence level of 0.75, which not only indicated a statistically significant improvement over the baseline classifier with unigram and negation features (F-score = 73.93%) but also surpassed the best performance obtained with other classifiers trained on generic lexicon features (F-score = 76.25%) and domain-specific lexicon features (F-score = 78.91%).

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References

  1. Constantinides, E., Fountain, S.J.: Web 2.0: Conceptual foundations and marketing issues. J. Direct Data Digit. Mark. Prac. 9, 231–244 (2008). doi:10.1057/palgrave.dddmp.4350098

    Article  Google Scholar 

  2. Ye, Q., Law, R., Gu, B., Chen, W.: The influence of user-generated content on traveler behavior: an empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Comput. Hum. Behav. 27, 634–639 (2011). doi:10.1016/j.chb.2010.04.014

    Article  Google Scholar 

  3. Zhang, Z., Ye, Q., Law, R., Li, Y.: The impact of e-word-of-mouth on the online popularity of restaurants: a comparison of consumer reviews and editor reviews. Int. J. Hosp. Manage. 29, 694–700 (2010). doi:10.1016/j.ijhm.2010.02.002

    Article  Google Scholar 

  4. Ruppenhofer, J.: Extending FrameNet for sentiment analysis. Revista Veredas 17, 66–81 (2013)

    Google Scholar 

  5. Ruppenhofer, J., Rehbein, I.: Semantic frames as an anchor representation for sentiment analysis. In: 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, pp. 104–109. ACL, Stroudsburg (2012)

    Google Scholar 

  6. Palmer, M., Gildea, D., Xue, N.: Semantic role labeling. Synth. Lect. Hum. Lang. Technol. 3, 1–103 (2010). doi:10.2200/S00239ED1V01Y200912HLT006

    Article  Google Scholar 

  7. Punyakanok, V., Roth, D., Yih, W.T.: The importance of syntactic parsing and inference in semantic role labeling. Comput. Linguist. 34, 257–287 (2008). doi:10.1162/coli.2008.34.2.257

    Article  Google Scholar 

  8. Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Fourth International Conference on Knowledge Discovery and Data Mining, pp. 80–86. AAAI Press, New York (1998)

    Google Scholar 

  9. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: ACL-02 Conference on Empirical Methods in Natural Language Processing, pp. 79–86. ACL, Stroudsburg (2002). doi:10.3115/1118693.1118704

  10. Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014). doi:10.1613/jair.4272

    Google Scholar 

  11. Mohammad, S.M., Kiritchenko, S., Zhu, X.: NRC-Canada: building the state-of-the-art in sentiment analysis of tweets. In: Second Joint Conference on Lexical and Computational Semantics, pp. 321–327. ACL, Stroudsburg (2013)

    Google Scholar 

  12. Cambria, E., Olsher, D., Rajagopal, D.: SenticNet 3: a common and common-sense knowledge base for cognition-driven sentiment analysis. In: Twenty-Eighth AAAI Conference on Artificial Intelligence, pp. 1515–1521. AAAI Press, New York (2014)

    Google Scholar 

  13. Ding, X., Liu, B., Yu, P.S.: A holistic lexicon-based approach to opinion mining. In: International Conference on Web Search and Data Mining, pp. 231–240. ACM, New York (2008). doi:10.1145/1341531.1341561

  14. Kaji, N., Kitsuregawa, M.: Building lexicon for sentiment analysis from massive collection of HTML documents. In: Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pp. 1075–1083. ACL, Stroudsburg (2007)

    Google Scholar 

  15. Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Conference on Human Language Technology and Empirical Methods in Natural Language Processing, pp. 347–354. ACL, Stroudsburg (2005). doi:10.3115/1220575.1220619

  16. Hu, M., 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). doi:10.1145/1014052.1014073

  17. Kim, S.M., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. In: Workshop on Sentiment and Subjectivity in Text, pp. 1–8. ACL, Stroudsburg (2006)

    Google Scholar 

  18. Gangemi, A., Presutti, V., Recupero, D.R.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comput. Intell. Mag. 9, 20–30 (2014). doi:10.1109/MCI.2013.2291688

    Article  Google Scholar 

  19. Palmer, M., Gildea, D., Kingsbury, P.: The proposition bank: an annotated corpus of semantic roles. Comput. Linguist. 31, 71–106 (2005). doi:10.1162/0891201053630264

    Article  Google Scholar 

  20. Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data, pp. 207–216. ACM, New York (1993). doi:10.1145/170036.170072

  21. Tan, S.-S., Na, J.-C.: Expanding sentiment lexicon with multi-word terms for domain-specific sentiment analysis. In: Morishima, A., Rauber, A., Liew, C.L. (eds.) ICADL 2016. LNCS, vol. 10075, pp. 285–296. Springer, Cham (2016). doi:10.1007/978-3-319-49304-6_34

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Wee Kim Wee School of Communication and Information, Nanyang Technological University, 31 Nanyang Link, Singapore, 637718, Singapore

    Sang-Sang Tan & Jin-Cheon Na

Authors
  1. Sang-Sang Tan
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  2. Jin-Cheon Na
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Correspondence to Sang-Sang Tan .

Editor information

Editors and Affiliations

  1. Faculteit der Geesteswetenschappen, Universiteit van Amsterdam , Amsterdam, The Netherlands

    Jaap Kamps

  2. Library & Information Center, University of Patras , Patras, Greece

    Giannis Tsakonas

  3. Aristotle University of Thessaloniki , Thessaloniki, Greece

    Yannis Manolopoulos

  4. Civil Engineering, University of Thrace , Kimmeria, Greece

    Lazaros Iliadis

  5. Informatics, Ionian University , Kerkyra, Greece

    Ioannis Karydis

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Tan, SS., Na, JC. (2017). Mining Semantic Patterns for Sentiment Analysis of Product Reviews. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds) Research and Advanced Technology for Digital Libraries. TPDL 2017. Lecture Notes in Computer Science(), vol 10450. Springer, Cham. https://doi.org/10.1007/978-3-319-67008-9_30

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  • DOI: https://doi.org/10.1007/978-3-319-67008-9_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67007-2

  • Online ISBN: 978-3-319-67008-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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