A Machine Learning Approaches for Natural Language Processing and Sentiment Analysis in Social Media

In the recent decade, there has been a rise in social media and various companies are using it as a primary tool for marketing. In order to better understand the emotion and views of the customers, social media sentiment analysis has been used. Machine learning is a rising technology which helps in sentiment analysis in the social media which helps to easily categorize the views and opinions of the customers in certain categories. The major objectives of the study include exploring Machine Learning Approaches for Natural Language Processing and Sentiment Analysis in social media. In this research, the methods of sentiment analysis in online social networks using ML and techniques for grouping have been studied in detail. A secondary research approach has been followed for the research. The major benefits of sentiment analysis on social media have also been presented as a part of the report. In the research it has been found that there are two major types of clarifying sentiments which are supervised and unsupervised learning. The research also reveals that the best learning model for evaluating the Twitter dataset is determined to be CNN using Word to Vector and under instruction.