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

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.

Empowerment of women's entrepreneurship in family business through Twitter

“…The machine learning classifiers applied for the prediction of the sentiment polarity of each tweet are:Multinomial Naïve-Bayes (MNB): this probabilistic classifier is based on the assumption that all the features are independent of each other given the context of the class, by applying the Bayes theorem (Song et al. , 2017)K-Nearest Neighbors (kNN): this is a classification algorithm that, depending on the distance between the train samples and the test data and the number of nearest neighbours, is able to predict the class of the result (Zerrouki et al. , 2020).…”

“…K-Nearest Neighbors (kNN): this is a classification algorithm that, depending on the distance between the train samples and the test data and the number of nearest neighbours, is able to predict the class of the result (Zerrouki et al. , 2020).…”

Determining the Intensity of Basic Emotions among People Suffering from Anorexia Nervosa Based on Free Statements about Their Body

“…The text analysis for detecting specific emotions is more complex than sentiment analysis [25][26][27]. However, it offers more benefits, as it more precisely depicts the author's attitude included in a note.…”