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Automatic Construction of Domain-Specific Sentiment Lexicons for Polarity Classification

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Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017 (PAAMS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 619))

Abstract

The article describes a strategy to build sentiment lexicons (positive and negative words) from corpora. Special attention will paid to the construction of a domain-specific lexicon from a corpus of movie reviews. Polarity words of the lexicon are assigned weights standing for different degrees of positiveness and negativeness. This lexicon is integrated into a sentiment analysis system in order to evaluate its performance in the task of sentiment classification. The experiments performed shows that the lexicon we generated automatically outperforms other manual lexicons when they are used as features of a supervised sentiment classifier.

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Notes

  1. 1.

    http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.htmll.

  2. 2.

    http://compprag.christopherpotts.net/code-data/imdb-words.csv.zip.

  3. 3.

    https://github.com/almatarneh/SPLM-Lexicon.

  4. 4.

    https://www.cs.cornell.edu/people/pabo/movie-review-data/.

  5. 5.

    http://ai.stanford.edu/~amaas/data/sentiment/.

References

  1. Augustyniak, L., Kajdanowicz, T., Kazienko, P., Kulisiewicz, M., Tuliglowicz, W.: An approach to sentiment analysis of movie reviews: lexicon based vs. classification. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 168–178. Springer (2014)

    Google Scholar 

  2. Benamara, F., Cesarano, C., Picariello, A., Recupero, D.R., Subrahmanian, V.S.: Sentiment analysis: adjectives and adverbs are better than adjectives alone. In: ICWSM. Citeseer (2007)

    Google Scholar 

  3. Gatti, L., Guerini, M., Turchi, M.: Sentiwords: deriving a high precision and high coverage lexicon for sentiment analysis. IEEE Trans. Affect. Comput. 7(4), 409–421 (2016)

    Article  Google Scholar 

  4. Huang, S., Niu, Z., Shi, C.: Automatic construction of domain-specific sentiment lexicon based on constrained label propagation. Knowl.-Based Syst. 56, 191–200 (2014)

    Article  Google Scholar 

  5. Hutto, C.J., Gilbert, E.: Vader: a parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014)

    Google Scholar 

  6. Kamps, J., Marx, M., Mokken, R.J., De Rijke, M., et al.: Using wordnet to measure semantic orientations of adjectives. In: LREC, vol. 4, pp. 1115–1118. Citeseer (2004)

    Google Scholar 

  7. Kim, S.M., Hovy, E.: Extracting opinions, opinion holders, and topics expressed in online news media text. In: Proceedings of the Workshop on Sentiment and Subjectivity in Text, pp. 1–8. Association for Computational Linguistics (2006)

    Google Scholar 

  8. 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, pp. 347–356. ACM (2011)

    Google Scholar 

  9. Lyu, K., Kim, H.: Sentiment analysis using word polarity of social media. Wireless Pers. Commun. 89(3), 941–958 (2016)

    Article  Google Scholar 

  10. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 142–150. Association for Computational Linguistics, Portland, June 2011. http://www.aclweb.org/anthology/P11-1015

  11. Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the ACL (2004)

    Google Scholar 

  12. Potts, C.: On the negativity of negation. Seman. Linguist. Theory 20, 636–659 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. 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 

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Author information

Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías da Información (CITIUS), Universidad de Santiago de Compostela, Rúa de Jenaro de la Fuente Domínguez, 15782, Santiago de Compostela, Spain

    Sattam Almatarneh & Pablo Gamallo

Authors
  1. Sattam Almatarneh
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  2. Pablo Gamallo
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Corresponding author

Correspondence to Sattam Almatarneh .

Editor information

Editors and Affiliations

  1. Departamento de Informática y Automática, Facultad de Ciencias, Universidad de Salamanca, Salamanca, Spain

    Fernando De la Prieta

  2. GECAD Instituto Superior de Engenharia do Porto, Porto, Portugal

    Zita Vale

  3. Department of Informática, Universidade de Lisboa Faculdade de Ciências, Lisboa, Portugal

    Luis Antunes

  4. Facultad de Ciencias, Departamento de Informática y Automática, Salamanca, Spain

    Tiago Pinto

  5. School of Computer Science, Dartmouth College, Hanover, New Hampshire, USA

    Andrew T. Campbell

  6. Departamento de Sistemas Informáticos y Computación, Universidad Politecnica de Valencia, Valencia, Spain

    Vicente Julián

  7. University of Aveiro, Institute of Electronics and Telematics Engineering, Aveiro, Portugal

    Antonio J.R. Neves

  8. Departamento de Informática y Automática, Facultad de Ciencias, University of Salamanca, Salamanca, Salamanca, Spain

    María N. Moreno

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Almatarneh, S., Gamallo, P. (2018). Automatic Construction of Domain-Specific Sentiment Lexicons for Polarity Classification. In: De la Prieta, F., et al. Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017. PAAMS 2017. Advances in Intelligent Systems and Computing, vol 619. Springer, Cham. https://doi.org/10.1007/978-3-319-61578-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-61578-3_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61577-6

  • Online ISBN: 978-3-319-61578-3

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