Twitter Sentiment Analysis Using Supervised Machine Learning

Abstract

Sentiment analysis aims to extract opinions, attitudes, as well as emotions from social media sites such as twitter. It has become a popular research area. The primary focus of the conventional way of sentiment analysis is on textual data. Twitter is the most renowned microblogging online networking site in which user posts updates related to different topics in the form of tweets. In this paper, a labeled dataset publicly available on Kaggle is used, and a comprehensive arrangement of pre-processing steps that make the tweets increasingly manageable to normal language handling strategies is structured. Since each example in the dataset is a pair of tweets and sentiment. So, supervised machine learning is used. In addition, sentiment analysis models based on naive Bayes, logistic regression, and support vector machine are proposed. The main intention is to break down sentiments all the more adequately. In twitter sentiment analysis, tweets are classified into positive sentiment and negative sentiment. This can be done using machine learning classifiers. Such classifiers will support a business, political parties, as well as analysts, etc., and so evaluate sentiments about them. By using training, data machine learning techniques correctly classify the tweets. So, this method doesn’t require a database of words, and in this manner, machine learning strategies are better and faster to perform sentiment analysis.