Optimizing Sentiment Analysis with Naïve Bayes and Random Forest Techniques: A Result-Based Approach

In the increased digitalization, the sentiment analysis and classification have evolved as an eminent, the sentiment analysis and classification have evolved as an eminent area to determine the polarity of positive, negative, and neutral reviews of the customers and users on products.

Authors

Publication Details

Journal
Journal of Artificial Intelligence Research & Advances
Published
2025-01-01
DOI
https://doi.org/10.37591/joaira.v12i02.204465
Primary Topic
Sentiment Analysis and Opinion Mining
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimizing Sentiment Analysis with Naïve Bayes and Random Forest Techniques: A Result-Based Approach

Shalini Puri, Chetan Kumar, Manupriya Nagar
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Journal of Artificial Intelligence Research & Advances
Sentiment Analysis and Opinion Mining
article

Optimizing Sentiment Analysis with Naïve Bayes and Random Forest Techniques: A Result-Based Approach

Shalini Puri, Chetan Kumar, Manupriya Nagar
article en

Abstract

In the increased digitalization, the sentiment analysis and classification have evolved as an eminent, the sentiment analysis and classification have evolved as an eminent area to determine the polarity of positive, negative, and neutral reviews of the customers and users on products.

Journal of Artificial Intelligence Research & Advances
Openalex Percentile: Top 95%
Sentiment Analysis and Opinion Mining
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Optimizing Sentiment Analysis with Naïve Bayes and Random Forest Techniques: A Result-Based Approach — Shalini Puri, Chetan Kumar, et al. · Journal of Artificial Intelligence Research & Advances (2025) | TGRS Research Map | TGRS