WalkToTopics: Inferring Topic Relations from a Feature Learning Perspective

Abstract

The increasing number of documents is leading to more and more topics nowadays. Understanding the relations between different topics evolved in documents become more important and challenging for users. Although many topic models have been devoted to analyzing topics, the study of topics’ potential relevances is still largely limited by various difficulties. Hence, we introduce WalkToTopics, an unsupervised topic mining and analysis model, for inferring potential relevances between different topics. Relying on an advanced feature learning technique to automatically summarize topic’s neighborhood features, WalkToTopics can reveal latent relations between different topics. Compared to existing approaches, our model is able to predict the relationship between any two individual topics of documents, and it does not require any prior knowledge of the existing topics’ relations and dictionaries. Moreover, WalkToTopics is a general model that also can work on exploring topic clusters or extracting sentiments, and can be applied to potential applications, such as ideas tracking and opinion summarization. Finally, we conducted two studies for common users and experts which both quantitatively and qualitatively demonstrate the effectiveness of WalkToTopics in helping users’ understanding of hidden relevances between topics on social media.