  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.
