A Branching Strategy For Unsupervised Aspect-based Sentiment Analysis

One of the most recent opinion mining research directions falls in the extraction of polarities referring to specific entities (called “aspects”) contained in the analyzed texts. The detection of such aspects may be very critical especially when the domain which documents belong to is unknown. 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 the used algorithm independent from the domain. In this work, we implemented different unsupervised solutions into an aspect-based opinion mining system. Such solutions are based on the use of semantic resources for performing the extraction of aspects from texts. The algorithms have been tested on benchmarks provided by the SemEval campaign and have been compared with the results obtained by domain-adapted techniques.