A Framework to Find Popularity of a Political Leader Using Emotion Mining

Abstract

Emotion mining is becoming an important part of the sentiment analysis. Online text sources are evolving into large-scale data repositories. These repositories contain valuable knowledge about human emotions like anger, disgust, fear, joy, sadness, and surprise. These emotional classes are useful in predicting the social trends. This work extracts emotions from twitter data and categorizes into six emotions. From these categories, one can identify the trending. On this basis, one can extract the popularity of a political leader in the society using tweets from his/her account. Score is calculated for each tweet of every leader and the popularity score is measured.