Machine Learning-Based Sentiment Analysis for the Social Media Platforms

Abstract

One of the most widely used social media platforms is Twitter. which is being used to express one’s opinions and sentiments. We can see a constant change in the pattern of tweets such as due to world limit users have started to use slangs, abbreviations and emoticons etc. which makes it difficult to identify the sentiments behind the tweets. Through this literature review we aim to describe methodologies, algorithms and procedure involved in creating a Machine Learning model that can analyze the sentiments. We have used dataset from “sentiment140 dataset” AND “TWITTER AND REDDIT SENTIMENT ANALYSIS DATASET”. This paper proposes the use of Logistic Regression Model, Linear SVC Model and Bernoulli NB Model for Sentiment Analysis. By comparing the three models with two different datasets we found that Linear SVC has greatest accuracy of 81%.