Twitter Sentiment Analysis using Enhanced TF-DIF Naive Bayes Classifier Approach

Public and private opinions on a wide range of topics are expressed and regularly disseminated through several social media channels. One of the social media platforms that is growing in popularity is Twitter. The emotional impact of a person plays an important role in daily life. The method of assessing a person's opinions and thought polarity is known as sentiment analysis. Here, this study addresses the sentiment classification problem on the Twitter dataset. A number of text preprocessing methods and Naive Bayes (NB) classifiers is used to perform sentiment analysis in the proposed system. Preprocessing procedures typically involve eliminating stop words, changing the case of the words to make them more normal, and using stemming or lemmatization. Twitter Sentiment Analysis is a method used for analyzing emotions from tweets. Tweets are useful in obtaining sentiment values from a user. The data provides an indication of polarity as positive, negative or unbiased values.