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Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

Abinash Tripathy, Santanu Kumar Rath

Source Title: International Journal of Rough Sets and Data Analysis (IJRSDA)4(1)

ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522515715|DOI: 10.4018/IJRSDA.2017010104

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MLA

Tripathy, Abinash, and Santanu Kumar Rath. "Classification of Sentiment of Reviews using Supervised Machine Learning Techniques." IJRSDA vol.4, no.1 2017: pp.56-74. http://doi.org/10.4018/IJRSDA.2017010104

APA

Tripathy, A. & Rath, S. K. (2017). Classification of Sentiment of Reviews using Supervised Machine Learning Techniques. International Journal of Rough Sets and Data Analysis (IJRSDA), 4(1), 56-74. http://doi.org/10.4018/IJRSDA.2017010104

Chicago

Tripathy, Abinash, and Santanu Kumar Rath. "Classification of Sentiment of Reviews using Supervised Machine Learning Techniques," International Journal of Rough Sets and Data Analysis (IJRSDA) 4, no.1: 56-74. http://doi.org/10.4018/IJRSDA.2017010104

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Abstract

Sentiment analysis helps to determine hidden intention of the concerned author of any topic and provides an evaluation report on the polarity of any document. The polarity may be positive, negative or neutral. It is observed that very often the data associated with the sentiment analysis consist of the feedback given by various specialists on any topic or product. Thus, the review may be categorized properly into any sort of class based on the polarity, in order to have a good knowledge about the product. This article proposes an approach to classify the review dataset made on basis of sentiment analysis into different polarity groups. Four machine learning algorithms viz., Naive Bayes (NB), Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA) have been considered in this paper for classification process. The obtained result on values of accuracy of the algorithms are critically examined by using different performance parameters, applied on two different datasets.

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Classification of Sentiment of Reviews using Supervised Machine Learning Techniques

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