Comparative Analysis of Different Supervised Machine Learning Algorithms for Sentiment Analysis

Gundeep Singh, Sukhmanjit Kaur, Prabhsimran Singh

Abstract


With advancement in technology, sentiment analysis has seen an exponential growth in last few years. Sentiment analysis is natural language processing technique which deals with emotion detection from given text. A major reason for popularity of this field is the fact it is applied to large number of application domains. Due to this, various techniques have been proposed to perform sentiment analysis using supervised machine learning algorithms. But a major question arises, that which algorithm is the best. This paper tries to provide solution of this question, by performing a comparative analysis of various algorithms taking different datasets under consideration. Our results show that Naïve Bayes out performs all other algorithms.

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References


Liu, Bing. "Sentiment analysis and opinion mining."Synthesis lectures on human language technologies 5, no. 1 (2012): 1-167. doi: 10.2200/S00416ED1V01Y201204HLT016.

Quinlan, J. Ross. "C4. 5: programs for machine learning". Elsevier, 2014.

Aha, David W., Dennis Kibler, and Marc K. Albert. "Instance-based learning algorithms." Machine learning 6, no. 1 (1991): 37-66.doi: 10.1007/BF00153759

McCallum, Andrew, and Kamal Nigam. "A comparison of event models for naive bayes text classification." In AAAI-98 workshop on learning for text categorization, vol. 752, pp. 41-48. 1998.

Schölkopf, Bernhard, Christopher JC Burges, and Alexander J. Smola, eds. Advances in kernel methods: support vector learning. MIT press, 1999.

Breiman, Leo. "Random forests." Machine learning 45, no. 1 (2001): 5-32. doi: 10.1023/A:1010933404324

Witten, Ian H., Eibe Frank, Mark A. Hall, and Christopher J. Pal. "Data Mining: Practical machine learning tools and techniques". Morgan Kaufmann, 2016.

McAuley, Julian, and Jure Leskovec. "Hidden factors and hidden topics: understanding rating dimensions with review text." In Proceedings of the 7th ACM conference on Recommender systems, pp. 165-172. ACM, 2013. doi: 10.1145/2507157.2507163

Maas, Andrew L., Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. "Learning word vectors for sentiment analysis." In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pp. 142-150. Association for Computational Linguistics, 2011.

Yelp Dataset, "https://www.yelp.com/dataset_challenge".




DOI: https://doi.org/10.23956/ijarcsse.v8i10.893

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