SENTIMENTAL ANALYSIS OF NEWS ARTICLES USING NAÏVE BAYES

Abstract: Sentiment analysis is one of the recent technologies under NLP (an application of Artificial intelligence and Machine Learning). It is used in many applications for recommendation and feedback analysis. In this paper, from defining sentiment analysis, to algorithms for sentiment analysis are discussed with practical results. The results declared in this paper are from the implantation of sentiment analysis on the news articles dataset using Naïve Bayes classifier. Additionally, the paper explores the various techniques employed in sentiment analysis, and delves into the challenges faced in accurately determining sentiment polarity. The experimental results demonstrate the effectiveness of the Naïve Bayes classifier in sentiment analysis, shedding light on its potential for enhancing decision-making processes in industries such as marketing, customer service, and public opinion analysis.

Keywords: Sentimental Analysis, News Articles, Target level Sentiment, Opinion Mining