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

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Author(s): Kadda Zerrouki (Higher School of Computer Science May 8, 1945, ESI Sidi Bel Abbes, Algeria), Reda Mohamed Hamou (GeCoDe Labs, University of Saida Dr Moulay Tahar, Algeria)and Abdellatif Rahmoun (Higher School of Computer Science May 8, 1945, ESI Sidi Bel Abbes, Algeria)
Copyright: 2022
Pages: 17
Source title: Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-6684-6303-1.ch030

Keywords: Data Analysis and Statistics / Data Mining / Engineering Science Reference / Library & Information Science

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

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