Comparative Study of different Machine Learning Algorithms to Analyze Sentiments with a Case Study of Two Person's Microblogs on Twitter
Anil Kumar, Purushottam Lal Bhari, Uday Pratap Singh, Vivek Saxena
Abstract
In recent years, social media has become a powerful tool for individuals to express their opinions and attitudes. Twitter, a microblogging platform, has emerged as a prominent source of such information. Sentiment analysis, the process of extracting attitudes and opinions from text data, has become increasingly popular in the field of machine learning. This study aims to perform a comparative analysis of different machine learning algorithms for sentiment analysis, with a case study of two individuals' microblogs on Twitter. A publicly available labeled dataset from Kaggle was used for the study. The pre-processing of the tweets was performed to make them manageable for common language management strategies. The study compared the performance of Naive Bayes, logistic regression, and support vector machine algorithms for sentiment analysis on the tweets. The results showed that all three algorithms performed well in classifying the sentiments, with support vector machines providing the highest accuracy. This study highlights the potential of machine learning algorithms in analyzing sentiments on Twitter and can serve as a reference for future research in this field.