Abstract
We live in the information age, where the amount of data readily available already overwhelms our capacity to analyze and absorb it without help from our machines. In particular, there is a wealth of text written in natural language available online that would become much more useful to us were we able to effectively aggregate and process it automatically. In this paper, we consider the problem of automatically classifying human sentiment from natural language written text. In this sentiment mining domain, we compare the accuracy of ensemble models, which take advantage of groups of learners to yield greater performance. We show that these ensemble machine learning models can significantly improve sentiment classification for free-form text.
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References
Lyman, P., and Varian, H. 2000. How much information. http://www.sims.berkeley.edu/how-muchinfo.
Lyman, P., and Varian, H. 2003. How much information 2003. http://www.sims.berkeley.edu/how-muchinfo-2003.
Breiman, L. 1996. Bagging predictors. Machine Learning 24(2):123–140.
Schapire, R. E. 2002. The boosting approach to machine learning: An overview.
Pang, B., and Lee, L. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In ACL, 271–278.
Pang, B.; Lee, L.; and Vaithyanathan, S. 2002. Thumbs up? sentiment classification using machine learning techniques. CoRR cs.CL/0205070.
Whitelaw, C.; Garg, N.; and Argamon, S. 2005. Using appraisal groups for sentiment analysis. In Herzog, O.; Schek, H.-J.; Fuhr, N.; Chowdhury, A.; and Teiken, W., eds., Proceedings of the 2005 ACM CIKM International Conference on Information and Knowledge Management, Bremen, Germany, October 31 - November 5, 2005, 625–631. ACM.
Snyder, B., and Barzilay, R. 2007. Multiple aspect ranking using the good grief algorithm. In Proceedings of NAACL HLT, 300–307.
Polikar, R. 2006. Ensemble based systems in decision making. IEEE Circuits and Systems Magazine Third Quarter:21–45.
Ho, Tin Kam. 1998. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(8): 832-844.
Demiriz, A., and Bennett, K. P. 2001. Linear programming boosting via column generation.
Domingo, and Watanabe. 2000. Madaboost: A modification of adaboost. In COLT: Proceedings of the Workshop on Computational Learning Theory, Morgan Kaufmann Publishers.
Chang, C. C., and Lin, C. J. 2001. LIBSVM: a library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
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Whitehead, M., Yaeger, L. (2010). Sentiment Mining Using Ensemble Classification Models. In: Sobh, T. (eds) Innovations and Advances in Computer Sciences and Engineering. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3658-2_89
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DOI: https://doi.org/10.1007/978-90-481-3658-2_89
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