Automated Topic Modeling and Sentiment Analysis of Tweets on SparkR
Prema Monish, Santoshi Kumari, C. Narendra Babu · 2018
Advancement of mobile and internet technology improves the communication and freedom of speaking in social networks, blogs and websites. Twitter is one of the most common and popular social media platform gives freedom to the people to put their views, thoughts and opinion to the world. Analyzing large scale tweets by putting together large scale individual's opinion on particular context will allows us to find various hidden topics and insights. This paper proposes for developing an automated topic modeling technique LDA to identify the interested topics of discussion from large scale tweets related to two famous political leaders of the county India. Paper implements a topic modeling method on SparkR framework to improve the speed and performance for large scale real time social data processing and analysis. Finally sentiment analysis of tweets is carried out using lexicon based approach to identify the people sentiment towards these two leaders. Using empirical results identified various unknown topics and people interest, expectations and their concerns on various topics. Results also shows automated topic modeling and sentiment analysis of tweets on SparkR framework improves the speed compare to normal R tool.