No Access

A Weighted Genetic Approach for Feature Selection in Sentiment Analysis

Fayçal Rédha Saidani

LARI Laboratory, Department of Computer Science, University of Mouloud Mammeri, Tizi-Ouzou 15000, Algeria

E-mail Address: faycal.saidani@ummto.dz

Corresponding author.

Search for more papers by this author

and

Idir Rassoul

LARI Laboratory, Department of Computer Science, University of Mouloud Mammeri, Tizi-Ouzou 15000, Algeria

E-mail Address: idir_rassoul@yahoo.fr

Search for more papers by this author

https://doi.org/10.1142/S1469026817500134Cited by:3 (Source: Crossref)

Abstract

The Sentiment Analysis has been witnessing a booming interest in recent years, due to the enormous growth of digital content, and various types of online reviews such as product and movie reviews. The aim of Sentiment Analysis is to use automated tools to detect and classify subjective information from these reviews. Feature selection happens to be an important step to extract and select more efficient text features, and at the same time to try improve the performance of the used classifier for Opinion Classification task. This paper proposes a methodology based on Genetic Algorithms to optimize the feature selection process for polarity classification. First, it uses a supervised weighting method in order to prune the searching space then, this weighting method is combined with stochastic search methods that generate the next feature subset in a heuristic manner. In order to validate the proposed method, we compared it with three feature selection methods on different sizes of feature subsets. The experimental results show the efficiency of our proposed method.

Keywords:

Remember to check out the Most Cited Articles!
Check out these titles in artificial intelligence!

References

1. C. Nopp and A. Hanbury, Detecting risks in the banking system by sentiment analysis, in Proc. EMNLP 15, (Lisbon, Portugal, 2015), pp. 591–600. Google Scholar
2. B. Pang, L. Lee and S. Vaithyanathan, Thumbs up?: Sentiment classification using machine learning techniques, in Proc. ACL-02 Conf. Empirical Methods in Natural Language Processing, (Philadelphia, 2002), pp. 79–86. Google Scholar
3. H. Xu, E. Santus, A. Laszlo and C. R. Huang, LLT-PolyU: Identifying sentiment intensity in ironic tweets, in Proc. 9th. Int. Works. Sem. Eval., (19–21 May 2015), Denver, pp. 673–678. Google Scholar
4. T. Wilson and S. Raaijmakers, Comparing word, character, and phoneme n-grams for subjective utterance recognition, in INTERSPEECH 2008, (22–26 September 2008), Brisbane, Australia, pp. 1614–1617. Google Scholar
5. B. He, C. Macdonald, J. He and I. Ounis, An effective statistical approach to blog post opinion retrieval, in Proc. 17th ACM Conf. Information and Knowledge Management, California, USA, pp. 1063–1072. Google Scholar
6. S. Gerani, M. J. Carman and F. Crestani, Investigating learning approaches for blog post opinion retrieval, in Proc. 31th European Conf. IR Research, eds. M. Boughanem, C. Berrut, J. Mothe and C. Soule-Dupuy (Berlin Heidelberg, 2009), pp. 313–324. Google Scholar
7. C. Lin and Y. He, Joint sentiment/topic model for sentiment analysis, in Proc. 18th ACM Conf. Information and Knowledge Management, (2–6 November 2009), Hong Kong, pp. 375–384. Google Scholar
8. B. Pang and L. Lee, Opinion mining and sentiment analysis, Found. Trends Inf. Retr. 2 (2008) 1–135. Crossref, Google Scholar
9. B. Liu, Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (Cambridge University Press, Cambridge, 2015). Crossref, Google Scholar
10. W. Medhat, A. Hassan and H. Korashy, Sentiment analysis algorithms and applications: A survey, Ain Shams Eng. J. 5 (2014) 1093–1113. Crossref, Google Scholar
11. E. Cambria, B. Schuller, Y. Xia and C. Havasi, New avenues in opinion mining and sentiment analysis, IEEE. Intell. Syst. 2 (2013) 15–21. Crossref, Google Scholar
12. E. Boiy and M. F. Moens, A machine learning approach to sentiment analysis in multilingual Web texts, Inf. Retr. 12 (2009) 526–558. Crossref, Google Scholar
13. A. Duric and F. Song, Feature selection for sentiment analysis based on content and syntax models, Decis. Support. Syst. 53 (2012) 704–711. Crossref, Google Scholar
14. M. Gamon, Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis, in Proc. 20th Int. Conf. Computational Linguistics (COLING’04), Association for Computational Linguistics (23–27 August 2004), Geneva, Switzerland, pp. 841–847. Google Scholar
15. T. OKeefe and I. Koprinska, Feature selection and weighting methods in sentiment analysis, in Proc. 14th Australasian Document Computing Symp. (December 2009) (School of Information Technology, University of Sydney, 2009), pp. 67–74. Google Scholar
16. Y. Wang, Z. Li, J. Liu, Z. He, Y. Huang and D. Li, Word vector modeling for sentiment analysis of product reviews, in Proc. Third CCF Conf. Natural Language Processing and Chinese Computing (NLPCC 2014), eds. C. Zong, J. Y. Nie, D. Zhao and Y. Feng (Springer, Berlin Heidelberg, 2014), pp. 68–180. Google Scholar
17. S. Tan and J. Zhang, An empirical study of sentiment analysis for chinese documents, Expert. Syst. Appl. 34 (2008) 2622–2629. Crossref, Google Scholar
18. Y. Lin, H. Lei, J. Wu and X. Li, An empirical study on sentiment classification of Chinese review using word embedding, arxiv preprint arXiv:1511.01665, 2015. Google Scholar
19. C. Nicholls and F. Song, Comparison of feature selection methods for sentiment analysis, in Proc. 23rd Canadian Conf. Artificial Intell., eds. A. Farzindar and V. Keselj (Springer, Berlin Heidelberg, 2010), pp. 286–289. Google Scholar
20. Y. Lin, J. Zhang, X. Wang and A. Zhou, An information theoretic approach to sentiment polarity classification, in Proc. 2nd Joint WICOW/AIRWeb Workshop on Web Quality (Web Quality’12), World Wide Web (16 April 2012), Lyon, France, pp. 35–40. Google Scholar
21. A. Abbasi, H. Chen and A. Salem, Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums, ACM. Trans. Inform. Syst. 26 (2008) 12. Crossref, Google Scholar
22. A. Onan and S. Korukoglu, A feature selection model based on genetic rank aggregation for text sentiment classification, J. Inform. Sci. 40, doi: 10.1177/0165551515613226. Google Scholar
23. P. Kalaivani and K. L. Shunmuganathan, Feature reduction based on genetic algorithm and hybrid model for opinion mining, Sci. Program. 2015 (2015) 12. Google Scholar
24. J. Martineau and T. Finin, Delta TFIDF: An improved feature space for sentiment analysis, ICWSM 9 (2009) 106. Google Scholar
25. C. Deisy, M. Gowri, S. Baskar, S. M. A. Kalaiarasi and N. Ramraj, A novel term weighting scheme MIDF for text categorization, J. Eng. Sci. Technol. 5 (2010) 94–107. Google Scholar
26. Z. H. Deng, K. H. Luo and H. L. Yu, A study of supervised term weighting scheme for sentiment analysis, Expert. Syst. Appl. 41 (2014) 3506–3513. Crossref, Google Scholar
27. M. A. Fattah, New term weighting schemes with combination of multiple classifiers for sentiment analysis, Neurocomputing 167 (2015) 434–442. Crossref, Google Scholar
28. J. H, Holland, Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (U Michigan Press, Ann Arbor, 1975). Google Scholar
29. A. A. Atun and N. Allahverdi, Neural network based recognition by using genetic algorithm for feature selection of enhanced fingerprints, in Proc. 8th Int. Conf. Adaptive and Natural Comput Algorithms, (ICANNGA), eds. B. Beliczynski, A. Dzielinski, M. Iwanowski and B. Ribeiro (Springer Verlag, Berlin Heidelberg, 2007), pp. 467–476. Google Scholar
30. A. Abbasi, Intelligent feature selection for opinion classification, IEEE Intell. Syst. 25 (2010) 75–79. Google Scholar
31. E. V. Kotelnikov and M. V. Pletneva, New term weighting schemes with combination of multiple classifiers for sentiment analysis, J. Comput. System Sci. Int. 55 (2016) 16. Google Scholar
32. B. Pang and L. Lee, A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts, in Proc. 42nd Annual Meeting on Association for Computational Linguistics, Association for Computational Linguistics (21–26 July 2004), Barcelona, p. 271. Google Scholar
33. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I. H. Witten, The WEKA data mining software: An update, ACM SIGKDD Explorations Newsletter 11 (2009) 10–18. Crossref, Google Scholar
34. K. W. Church and P. Hanks, Word association norms, mutual information, and lexicography, Comput. Linguist 16 (2009) 22–29. Google Scholar
35. R. Prabowo and M. Thelwall, Sentiment analysis: A combined approach, J. Informetr. 3 (2009) 143–157. Crossref, Google Scholar
36. L. K. W. Tan, J. C. Na and Y. L. Theng, Phrase-level sentiment polarity classification using rule-based typed dependencies and additional complex phrases consideration, J. Comput. Sci. Technol. 27 (2012) 650–666. Crossref, Google Scholar
37. Y. Yu, W. Duan and Q. Cao, The impact of social and conventional media on firmequity value: A sentiment analysis approach, Decis. Support Syst. 55 (2013) 919–926. Crossref, Google Scholar
38. S. Borzsonyi, D. Kossmann and K. Stocker, The skyline operator, in, Proc. 17th Int. Conf., Data Engineering (2–6 April 2001), California, pp. 421–430. Google Scholar