Opinion mining of news headlines using SentiWordNet

Abstract

Opinion Mining (also known as “Sentiment Analysis”) is an area of text classification which continuously gives its contribution in research field. The main objective of Opinion mining is Sentiment Classification i.e. to classify the opinion into positive or negative classes. SentiWordNet is an opinion lexicon derived from the WordNet database where each term is associated with some numerical scores indicating positive and negative sentiment information. Up until recently most researchers presented opinion mining of online user generated data like reviews, blogs, comments, articles etc. Opinion mining for offline user generated data like newspaper is unconcerned so far despite the fact that it is also explored by many users. As a first step, this paper present opinion mining for newspaper headlines using SentiWordNet. Further, most of the researchers implement the opinion mining by separating out the adverb-adjective combination present in the statements or classifying the verbs of statements. On the other hand, in this paper we analyze each and every word in the News headline whether it is a noun, verb, adverb, adjective or any other part-of-speech. During experiment, python packages are used to classify words. Then SentiWordNet 3.0 is used to identify the positive and negative score of each word thus evaluating the total positive/negative impact in that news headline.