Abstract
Sentiment analysis or opinion mining is the computational study of people's opinion, sentiments, attitudes and emotions expressed in written language. It is one of the most active research areas in natural language processing and text mining in recent years. It is a way to analyze the subjective information in the text and then mine the opinion. Sentiment analysis is the procedure by which information is extracted from the opinion appraisal and emotions of people in regards to entities, events and their attributes. In decision making, the opinion of others have a significant effect on customer ease, making choices with regards have a significant effect, product entity. The approach of text sentiment analysis typically works at a particular level like phrase, sentence or document level. This project aims at analyzing a solution for the sentiment identification at a fine-grained level,namely the sentence level in which polarity of the sentence can be given by three categories as positive, negative and neutral. The data set is gathered from inshorts.com and the project is restricted to 3 news article domains namely- Sports, World and Politics. Lexicon based approach is used for Sentiment Analysis. VADER gives the polarity of negativity, neutrality, positivity and also the consolidated compound score for the given text. The Data Labeling, Data Processing and Finalization is done using Compound score from VADER.
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