Personalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model

Abstract

People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people's opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person's past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research.

A Personalized Sentiment Model with Textual and Contextual Information

“…In our previous works, we have evaluated the effectiveness of considering individual differences in sentiment analysis by employing a conceptbased representation and the static or universal Hawkes process (Guo et al, 2018(Guo et al, , 2019a. In this paper, we advance the development by adopting five input formulations with different combinations of granular levels, and propose a refined model with user-specific Hawkes process to constitute a step forward in capturing the nuances of user dynamics and providing insights in the personalized modeling.…”