Real time analysis of social media data to understand people emotions towards national parties
Santoshi Kuamri, C. Narendra Babu · 2017
Social media has given new way of communication technology for people to share their opinions, interest, sentiments. Huge amount of unstructured data is generated from social media like Facebook, twitter, LinkedIn, which is repository of useful insights. Analytics can be applied to extract various useful insights form this. Main objective of this paper is to extract the knowledge from large social media data, identify the people sentiments and behavior to make cognizant decisions. Effective sentiment analysis of people emotions is necessary for complex topics like politics, government, present trends and healthcare. These objective are achieved by real time retrieval of twitter data and perform sentiment analysis. It helps to classify emotions of the people are positive, negative, happy, sad or neutral towards national parties of India (BJP and INC). Considering people emotions, parties can identify their weakness and look after improvement to fulfill society needs and work towards satisfying people requirements. Using text mining and unsupervised lexical method classified tweets related to these parties to identify people emotions for the parties.