Predicting Emotion Dynamics Sequence on Twitter via Deep Learning Approach

[EN] Exploring the mechanism about users' emotion dynamics towards social events and further predicting their future emotions have attracted great attention to the researchers. 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 influenced by his/her friends. 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 an Emotion-based User Sequential Influence Model (E-USIM) 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 then apply the developed model to predict users' future emotions by combing of personal and interpersonal influence.