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Sentiment Analysis and Evaluation of Movie Reviews Using Classifiers

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Cognitive Informatics and Soft Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1317))

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Abstract

The analysis of the opinions and likelihood as well as emotions in any form either in the form of text is called as sentiment analysis. Opinion mining is termed as sentiment analysis. The analysis of the data is very helpful in expressing the likelihood of the options for group of persons or individuals. With the advancement of the Internet, a huge collection data is being generated. Facebook, Twitter, YouTube, LinkedIn, Instagram and other social sites are gaining a lot of popularity as they allow users from different parts of the world to share their views upon various topics through comments, posts, tweets and tags. This paper provides a survey of existing technique for sentimental analysis like machine learning-based approaches like Naïve Bayes, logistic regression and SVM. Among them, decision tree recorded the best performance. Decision tree generated a classification accuracy of 91.6% and a minimum execution time of 1.12 s. Hence, it can be concluded that sentiment analysis with decision tree algorithm is an optimal concept for efficient movie review analysis.

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Mallick, P.K., Dutta, P., Mishra, S., Mishra, M.K. (2021). Sentiment Analysis and Evaluation of Movie Reviews Using Classifiers. In: Mallick, P.K., Bhoi, A.K., Marques, G., Hugo C. de Albuquerque, V. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1317. Springer, Singapore. https://doi.org/10.1007/978-981-16-1056-1_5

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