Abstract
This paper is devoted to the comparison of different common base and ensemble classifiers for sentiment classification of reviews. It is also aimed to generate different feature sets and to observe their contribution to the classification accuracy. In detail, these feature sets are formed in an hierarchical manner, which is accomplished by first forming part-of-speech (POS) based word groups and then utilizing feature frequencies, SentiWordNet scores and their combination to obtain feature sets. In addition, several common base classifiers, namely Multinominal Naive Bayes (MNB), Support Vector Machine (SVM), Voted Perceptron (VP), K-Nearest Neighbor (k-NN), as well as common ensemble strategies, Random Forests (RFs), Stacking and Random Subspace (RSS) are each tested on the generated feature sets. Also, the Behavior-Knowledge Space (BKS) method has been derived to be applied on the set of outcomes for different algorithm and feature set combinations. Furthermore, a probability based meta-classifier technique has been tested on this set of outcomes. Finally, Information Gain (IG) feature selection technique has been applied to reduce the feature spaces. The experiments are conducted on a widely used movie review dataset and an equally common multi-domain review dataset. The results indicate that the probabilistic ensemble method generally gives comparatively better results than the other algorithms tested on the chosen datasets and that IG method can be utilized to save computational time while maintaining allowable accuracy.
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References
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)
Kim, H., Ganesan, K., Sondhi, P., Zhai, C.: Comprehensive review of opinion summarization (survey) (2011)
Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)
Li, S., Zhang, H., Xu, W., Chen, G., Guo, J.: Exploiting combined multi-level model for document sentiment analysis. In: International Conference on Pattern Recognition, IEEE Computer Society Washington, pp. 4141–4144 (2010)
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. ACL ’04, Stroudsburg, Association for Computational Linguistics (2004)
Koncz, P., Paralic, J.: An approach to feature selection for sentiment analysis. In: International Conference on Intelligent Engineering Systems, Poprad, Slovakia (2011). June 23–25, 2011
Varma, S.: Cross-product sentiment analysis via ensemble svm classifiers. In: International Conference on Advancements in Information Technology (2011). Dec 17–18, 2011
Jiang, L., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Association for Computational Linguistics (ACL) (2011)
Bermingham, A., Smeaton, A.: Classifying sentiment in microblogs:is brevity an advantage? In: International Conference on Information and Knowledge Management, pp. 1833–1836 (2010)
Claster, W.B., Hung, D.Q., Shanmuganathan, S.: Unsupervised artificial neural nets for modeling movie sentiment. In: Second International Conference on Computational Intelligence (2010)
Li, G., Hoi, S.C.H., Chang, K., Jain, R.: Micro-blogging sentiment detection by collaborative online learning. In: IEEE International Conference on Data Mining (2010)
Chenlo, J., Losada, D.E.: An empirical study of sentence features for subjectivity and polarity classification. Inf. Sci. 280, 275–288 (2014)
Xia, R., Zong, C., Li, S.: Ensemble of feature sets and classification algorithms for sentiment classification. Inf. Sci. 181(6), 1138–1152 (2011)
Huang, P., Wang, G., Qin, S.: Boosting for transfer learning from multiple data sources. Pattern Recognit. Lett. 33(5), 568–579 (2012)
Li, W., Wang, W., Chen, Y.: Heterogeneous ensemble learning for chinese sentiment classification. J. Inf. Comput. Sci. 9(15), 4551–4558 (2012)
Zhang, Z., Miao, D., Wei, Z., Wang, L.: Document-level sentiment classification based on behavior-knowledge space method. Adv. Data Min. Appl. Lect. Notes Comput. Sci. 7713(15), 330–339 (2012)
Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC’10), Valletta, Malta, European Language Resources Association (ELRA) (2010)
McCallum, A., Nigam, K.: A comparison of event models for naive Bayes text classification. In: Learning for Text Categorization: Papers from the 1998 AAAI Workshop, pp. 41–48 (1998)
Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods—Support Vector Learning, MIT Press (1998)
Altman, N.S.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 46(3), 175–185 (1992)
Su, Y., Zhang, Y., Ji, D., Wang, Y., Wu, H.: Ensemble learning for sentiment classification. Lect. Notes Comput. Sci. 7717, 84–93 (2013)
Oza, N.C.: Online Ensemble Learning. Ph.D. thesis, The University of California, Berkeley (2001)
Dietterich, T.G.: Ensemble methods in machine learning. In: Proceedings of the First International Workshop on Multiple Classifier Systems. MCS ’00, Springer, London, pp. 1–15 (2000)
Huang, Y.S., Suen, C.Y.: A method of combining multiple experts for the recognition of unconstrained handwritten numerals. IEEE Trans. Pattern Anal. Mach. Intell. 17(1), 90–94 (1995)
Blitzer, J., Dredze, M., Pereira, F.: Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In: Proceedings of the Association for Computational Linguistics (ACL) (2007)
Li, S., Xia, R., Zong, C., Huang, C.: A framework of feature selection methods for text categorization. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2- Volume 2. ACL ’09, Association for Computational Linguistics, Stroudsburg, pp. 692–700 (2009)
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Aldogan, D., Yaslan, Y. (2016). A Comparison Study on Ensemble Strategies and Feature Sets for Sentiment Analysis. In: Abdelrahman, O., Gelenbe, E., Gorbil, G., Lent, R. (eds) Information Sciences and Systems 2015. Lecture Notes in Electrical Engineering, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-319-22635-4_33
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