Use of Machine Learning Models for Recommender System of Sentiment Analysis

Abstract

The study proposes an effective sentiment analysis recommender system framework using machine learning models. Recommender systems are used to build recommendations by processing information from actively gathered varied kinds of data. The data that is used for processing information depends upon the type of recommender system. In recent years, with the rapid growth of Internet technology, online shopping has become a rapid way for users to purchase and consume desired products. Tweet sentiment analysis is a product of the vast amount of user-generated content on social media platforms like Twitter. Sentiment analysis serves as the foundation for recommendation and decision support systems, and it is becoming a crucial tool on online platforms to extract user emotional state data and increase user happiness.