Lexicon Based Approach for Sentiment Analysis on News Articles Using Deep Learning Techniques

Sentiment Analysis or Opinion Mining is a most popular eld to analyze and nd out insights from text data from various sources like News Articles, Twitter, etc. The medium of publishing news and events has become faster with the advancement of Information Technology (IT). IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. The medium of publishing news and events has become faster with the advancement of Information Technology (IT). IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. The medium of publishing news and events has become faster with the advancement of Information Technology (IT). IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. Manual traversal of such huge data is a challenging job; thus, sophisticated methods are acquired to perform this task automatically and efficiently. News reports events that comprise of emotions good, bad, neutral. Sentiment analysis is utilized to investigate human emotions (i. e. , sentiments) present in textual information. This paper presents a lexicon-based approach for sentiment analysis of news articles. The experiments have been performed on BBC news dataset, which expresses the applicability and validation of the adopted approach. After preprocessing we applied machine learning algorithms to classify reviews that are positive or negative. This paper concludes that, Deep Learning Techniques gives best results to classify the News Articles Reviews. LDA got accuracy 95.17 % and Word2vec got accuracy 93.54 % for Emotional valence Reviews.