Emotion Mining in Social Media Data

Abstract

Emotions are known to influence the perception of human beings along with their memory, thinking and imagination. Human perception is important in today’s world in a wide range of factors including but not limited to business, education, art, and music. Microblogging and Social networking sites like Twitter, Facebook are challenging sources of information that allow people to share their feelings and thoughts on a daily basis. In this paper we propose an approach to automatically detect emotions on Twitter messages that explores characteristics of the tweets and the writer’s emotion using Support Vector Machine LibLinear model and achieve 98% accuracy. Emotion mining gained attraction in the field of computer science due to the vast variety of systems that can be developed and promising applications like remote health care system, customer care services, smart phones that react based on users’s emotion, vehicles that sense emotion of the driver. These emotions help understand the current state of user. In order to perform suitable actions or provide suggestions on how user’s can enhance their feeling for a better healthy life-style we use actionable recommendations. In this work we extract action rules with respect to the user emotions that help provide suggestions for user’s.