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Sentiment Analysis of Twitter Comments Using Naive Bayes Classifier
- 1 China University of Geosciences
* Author to whom correspondence should be addressed.
Abstract
Social media has a significant role in how people express their emotions and elaborate on their opinions in today's culture. There are many new forms of social media, and Twitter is one of them. In this experiment, the sentiment of pre-processed Twitter comment data was examined using naive Bayes and logistic regression techniques. In order to categorise the emotional tendency of text for Twitter comments, a naive Bayesian classifier is created. In processing this material, the Naive Bayes and logistic regression models' benefits and drawbacks are compared and summarised. Naive Bayes can achieve good accuracy with binary emotion analysis. The accuracy of the naive Bayes model is 0.06 points higher than that of logic training under identical processing settings, and the recall rate is 0.05 points higher.
Keywords
naive bayes, sentiment analysis, Twitter comment, natural language processing, machine learning
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Cite this article
Zhang,Z. (2023). Sentiment Analysis of Twitter Comments Using Naive Bayes Classifier. Communications in Humanities Research,10,262-268.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Educational Innovation and Philosophical Inquiries
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