CRATS: An LDA-Based Model for Jointly Mining Latent Communities, Regions, Activities, Topics, and Sentiments from Geosocial Network Data

Abstract

Geosocial networks like Yelp and Foursquare have been rapidly growing and accumulating plenty of data such as social links between users, user check-ins to venues, venue geographical locations, venue categories, and user textual comments on venues. These data contain rich knowledge on the user's social interactions in communities, geographical mobility patterns between regions, categorical preferences on activities, aspect interests in topics, and opinion expressions for sentiments. Such knowledge is essential for two key applications, namely, text sentiment classification and venue recommendations, which will be developed in this paper. To extract the knowledge from the data, the key task is to discover the latent communities, regions, activities, topics, and sentiments of users. However, these latent variables are interdependent, e.g., users in the same community usually travel on nearby regions and share common activities and topics, which renders a big challenge for modeling these latent variables. To tackle this challenge, in this study, we propose an LDA-based model called CRATS that jointly mines the latent Communities, Regions, Activities, Topics, and Sentiments based on the important dependencies among these latent variables. To the best of our knowledge, this is the first study to jointly model these five latent variables. Finally, we conduct a comprehensive performance evaluation for CRATS in different applications, including text sentiment classification and venue recommendations, using three large-scale real-world geosocial network data sets collected from Yelp and Foursquare. Experimental results show that CRATS achieves significantly superior performance against other state-of-the-art techniques.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 29 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.