Generalized Association Rules for Sentiment Analysis in Twitter

Abstract

Association rules have been widely applied in a variety of fields over the last few years, given their potential for descriptive problems. One of the areas where the association rules have been most prominent in recent years is social media mining. In this paper, we propose the use of association rules and a novel generalization of these based on emotions to analyze data from the social network Twitter. With this, it is possible to summarize a great set of tweets in rules based on 8 basic emotions. These rules can be used to categorize the feelings of the social network according to, for example, a specific character.