A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media
This work addresses sentiment analysis for social media users, but it appears incremental as it builds on existing graph-based methods with specific modifications.
The authors tackled the problem of predicting user-level sentiments for specific topics in social media by proposing a semi-supervised approach using a heterogeneous graph model, which incorporates user influences and multiple link types, achieving unspecified results without concrete numbers.
Social Media has seen a tremendous growth in the last decade and is continuing to grow at a rapid pace. With such adoption, it is increasingly becoming a rich source of data for opinion mining and sentiment analysis. The detection and analysis of sentiment in social media is thus a valuable topic and attracts a lot of research efforts. Most of the earlier efforts focus on supervised learning approaches to solve this problem, which require expensive human annotations and therefore limits their practical use. In our work, we propose a semi-supervised approach to predict user-level sentiments for specific topics. We define and utilize a heterogeneous graph built from the social networks of the users with the knowledge that connected users in social networks typically share similar sentiments. Compared with the previous works, we have several novelties: (1) we incorporate the influences/authoritativeness of the users into the model, 2) we include comment-based and like-based user-user links to the graph, 3) we superimpose multiple heterogeneous graphs into one thereby allowing multiple types of links to exist between two users.