For Comparison of Predictive Models for Sentiment Analysis Using Twitter Tweets

Project Code :TCMAPY936

Objective

The main objective of the comparison of predictive models for sentiment analysis using Twitter tweets is to evaluate and compare the performance of different machine learning algorithms in accurately predicting sentiment from Twitter data, aiming to identify the most effective model for sentiment analysis tasks.

Abstract

Social media like twitter and Facebook is seen as a space where public opinions are formed in today’s world. The data from these tweets and posts can provide valuable insights for policy makers and other agencies to propose and implement policies better. An attempt is made in this paper to understand the public opinion on the recently implemented demonetization policy in India. A sentiment analysis is carried out on twitter data set using machine learning approaches. Twitter data from November 22nd 2016 to November 23rd 2016 are considered for analysis. The data set is pre-processed for cleaning the data and making it possible for analysis. A ﬁnal set of 8000 tweets are analyzed using machine learning techniques like Linear Regression, Naïve Bayes, Random forest and Decision tree classifier and the results are compared.

KEYWORDS: Twitter data, Sentiment analysis, Machine learning, Demonetization, Linear Regression, Naïve Bayes, Decision Tree and Random Forest.

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