Sentiment Analysis and Topic Modeling on News Headlines

Abstract

In today’s world of competitiveness, sentiment analysis has wide range of applications from medical perspective to examine the mental situation of the person to entertainment industry, corporates, politics and so on in order to examine the perspective and views of the people towards their product. News media play vital role in shaping the views of public regarding any product or people. The dataset used for this thesis is headlines dataset of one of the leading new portals of India i.e., Times of India. Both supervised and unsupervised techniques would be used to perform the analysis on the dataset. The thesis has two aspects i.e., first, sentiment analysis for which supervised technique Bi-LSTM will be used and second, topic modeling for which unsupervised techniques LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Analysis) will be compared, and then the best performing algorithm will be used for topic classification. The topics identified will be used to classify the dataset so that prediction of topic for particular headline can be done.