An Unsupervised Particle Swarm Optimization Approach for Opinion Clustering

Supervised machine learning (ML) and lexicon-based are the most frequent approaches for opinion mining (OM), but they require considerable effort for preparing the training data and to build the opinion lexicon, respectively. This paper presents two unsupervised approaches for OM based on Particle Swarm Optimization (PSO). The PSO-based approaches were evaluated by eighteen experiments with different corpora types, domains, language, class balancing and pre-processing techniques. The proposed approaches achieved better accuracy on twelve experiments. Best results were obtained on corpora with a reduced number of dimensions and for specific domains. Best accuracy (0.79) was obtained by Discrete IDPSO on the OBCC corpus, outperforming supervised ML and lexicon-based approaches for this corpus.