Sentiment Analysis of Tweets Using Naïve Bayes, KNN, and Decision Tree
2020 ◽
Vol 10
(4)
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pp. 35-49
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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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2021 ◽
Vol 8
(1)
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pp. 50-56
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2016 ◽
Vol 1
(1)
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pp. 17
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