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Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree

Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree

Kadda Zerrouki, Reda Mohamed Hamou, Abdellatif Rahmoun
Copyright: © 2020 |Volume: 10 |Issue: 4 |Pages: 15
ISSN: 1947-9344|EISSN: 1947-9352|EISBN13: 9781799806523|DOI: 10.4018/IJOCI.2020100103
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MLA

Zerrouki, Kadda, et al. "Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree." IJOCI vol.10, no.4 2020: pp.35-49. http://doi.org/10.4018/IJOCI.2020100103

APA

Zerrouki, K., Hamou, R. M., & Rahmoun, A. (2020). Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree. International Journal of Organizational and Collective Intelligence (IJOCI), 10(4), 35-49. http://doi.org/10.4018/IJOCI.2020100103

Chicago

Zerrouki, Kadda, Reda Mohamed Hamou, and Abdellatif Rahmoun. "Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree," International Journal of Organizational and Collective Intelligence (IJOCI) 10, no.4: 35-49. http://doi.org/10.4018/IJOCI.2020100103

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Abstract

Making use of social media for analyzing the perceptions of the masses over a product, event, or a person has gained momentum in recent times. Out of a wide array of social networks, the authors chose Twitter for their analysis as the opinions expressed there are concise and bear a distinctive polarity. Sentiment analysis is an approach to analyze data and retrieve sentiment that it embodies. The paper elaborately discusses three supervised machine learning algorithms—naïve bayes, k-nearest neighbor (KNN), and decision tree—and compares their overall accuracy, precision, as well as recall values, f-measure, number of tweets correctly classified, number of tweets incorrectly classified, and execution time.

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