An unsupervised aspect extraction strategy for monitoring real-time reviews stream

One of the most important opinion mining research directions falls in the extraction of
polarities referring to specific entities (aspects) contained in the analyzed texts. The detection
of such aspects may be very critical especially when documents come from unknown
domains. Indeed, while in some contexts it is possible to train domain-specific
models for improving the effectiveness of aspects extraction algorithms, in others the
most suitable solution is to apply unsupervised techniques by making such algorithms
domain-independent. Moreover, an emerging need is to exploit the results of aspect-based
analysis for triggering actions based on these data. This led to the necessity
of providing solutions supporting both an effective analysis of user-generated content
and an efficient and intuitive way of visualizing collected data. In this work, we implemented
an opinion monitoring service implementing (i) a set of unsupervised strategies
for aspect-based opinion mining together with (ii) a monitoring tool supporting users
in visualizing analyzed data. The aspect extraction strategies are based on the use of semantic
resources for performing the extraction of aspects from texts. The effectiveness
of the platform has been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domain-adapted techniques.