Modelling Emotion Dynamics on Twitter via Hidden Markov Model

Abstract

Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. One of the unexplored components of human communication found online in written form is an emotional expression. However, despite the concreteness of the online expressions in written form, it remains unpredictable which kinds of emotions will be expressed in individual messages of Twitter users. To investigate this, we perform an investigation on observing emotions unfolding in a consecutive sequence of tweets for a particular user based on his/her past history. In this paper, we propose a method on given a set of tweets related with some events (identified by the usage of a hashtag), determines how those sentiments will be distributed on behalf of a person within a conversation. We present the Hidden Markov Model (HMM) to understand the nature of emotion dynamics in Twitter messages.