Sentiment Analysis using deep learning for use in recommendation systems of various public media applications
Karuna Arava, Rudraraju Sri Krishna Chaitanya, Shaik Sikindar, S. Phani Praveen, D. Swapna
Abstract
Sentiment Analysis is a method of analyzing text and extracting opinions from it. It’s also known as emotion or opinion extraction, and it’s part of the machine learning as well as data mining categories. There are numerous ways to convey one’s sentiments. It can be articulated in a variety of ways, such as through feelings, making judgments, or expressing one’s vision or insight. Sentiment investigation is the act of detecting, recognizing, and categorizing a user’s emotion or view for any service, such as movies, product issues, events, or any other attribute that can be good, negative, or neutral. This analysis is based on social communication channels such as websites that included ratings, forum conversations, blogs, micro blogs, Twitter, and other social media platforms. The important goal of suggested system is to improvise accuracy and to generate recommendation system using deep learning algorithms.