Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews

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Published: Sep 1, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i7.7839
Keywords:
Machine Learning, Logistic Regression, Naïve Byes, Sentiment Analysis, Polarity

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Rahul Kumar Sharma
School of Computing Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh
Arvind Dagur
School of Computing Science & Engineering, Galgotias University, Greater Noida, Uttar Pradesh

Abstract

To determine an author's emotional state from their written words is the focus of sentiment analysis, a subfield of NLP. This study focuses on the many techniques used to categorize the text reviews written in natural language according to the viewpoints expressed therein, in order to determine if the widespread behavior is positive, negative, or neutral. Streaming of thoughts and expression of opinion have been facilitated by the proliferation of debate forums, Weblogs, product review sites, e-commerce, and social networking sites. A lot of people's feelings, reviews, and assessments of others' opinions can be found on social media. This research ranks the top classifier for feelings using data derived from online product reviews posted to Twitter. Experimental work on polarity classification with well-known classifiers such as Naive byes, Support vector machine, and Logistic regression for anticipating testimonials was addressed.

Article Details

How to Cite
Sharma, R. K. ., & Dagur, A. . (2023). Analyzing the Performance of Different Classifier for Detecting Polarity of Customer Reviews. International Journal on Recent and Innovation Trends in Computing and Communication, 11(7), 142–148. https://doi.org/10.17762/ijritcc.v11i7.7839
Issue
Vol. 11 No. 7 (2023)
Section
Articles

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