A Personalized Sentiment Model with Textual and Contextual Information

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person{'}s expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person{'}s past expressions, and offer a better understanding of the sentiment from the expresser{'}s perspective. Additionally, we investigate how a person{'}s sentiment changes over time so that recent incidents or opinions may have more effect on the person{'}s current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information.