Opinion mining, also known as sentiment analysis, is the technique of extracting the polarity of sentiments such as positive, negative, and neutral from natural languages, particularly English Now a days, people use and express themselves through social media like Facebook, Instagram, Twitter etc. These opinions expressed by people on various events like product advertisements, social issues, elections, etc., can be used in decision making However, going through every comment or viewpoint made on social media is extremely tough. As a result, in this work, we conduct sentiment analysis on Twitter data focused on the 2020 Presidential Election in the United States. Tweets are extracted through twitter API from twitter.com. By using Vader as a model in the Extract Sentiment operator of RapidMiner 9.10, sentiments are extracted as positive, negative, and neutral. In addition, supervised classification techniques such as Decision tree, KNN, and Nave Bayes are used to test the model’s correctness. Among three classification algorithms, Naive Bayes performs well on Twitter data with a performance accuracy of 60.69%.

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