Viscovery: Trend Tracking in Opinion Forums based on Dynamic Topic Models
This work addresses the gap between user opinions and organizational actions for enterprises or government agencies, though it appears incremental as it extends existing dynamic topic models with incremental learning and sentiment analysis.
The authors tackled the problem of tracking opinions in forums by developing Viscovery, a platform that uses dynamic topic models for opinion summarization and trend tracking, achieving near-real-time model updates through incremental learning and incorporating sentiment analysis for granular sentiment tracking.
Opinions in forums and social networks are released by millions of people due to the increasing number of users that use Web 2.0 platforms to opine about brands and organizations. For enterprises or government agencies it is almost impossible to track what people say producing a gap between user needs/expectations and organizations actions. To bridge this gap we create Viscovery, a platform for opinion summarization and trend tracking that is able to analyze a stream of opinions recovered from forums. To do this we use dynamic topic models, allowing to uncover the hidden structure of topics behind opinions, characterizing vocabulary dynamics. We extend dynamic topic models for incremental learning, a key aspect needed in Viscovery for model updating in near-real time. In addition, we include in Viscovery sentiment analysis, allowing to separate positive/negative words for a specific topic at different levels of granularity. Viscovery allows to visualize representative opinions and terms in each topic. At a coarse level of granularity, the dynamic of the topics can be analyzed using a 2D topic embedding, suggesting longitudinal topic merging or segmentation. In this paper we report our experience developing this platform, sharing lessons learned and opportunities that arise from the use of sentiment analysis and topic modeling in real world applications.