A holistic model of mining product aspects and associated sentiments from online reviews

Online product reviews are considered a significant information resource useful for both potential customers and product manufacturers. In order to extract the fundamental product aspects and their associated sentiments from those reviews of plain texts, aspect-based sentiment analysis has emerged and has been regarded as a promising technology. This paper proposes a novel model to realize aspect-based sentiment summarization in an integrative way: composing the system with consistently designed feature extraction and clustering, collocation orientation disambiguation, and sentence sentiment strength calculation. Collocations of product features and opinion words are initially extracted through pattern-based bootstrapping. A novel confidence estimation method considering two measurements, Prevalence and Reliability, is exploited to assess both patterns and features. The obtained features are further clustered into aspects. Each cluster is assigned a weight based on arithmetic means of feature similarities and confidences. The orientations of dynamic sentiment ambiguous adjectives (DSAAs) are then determined within opinion collocations. Finally, sentiment strengths of opinion clauses for each aspect are computed according to a set of fine-grained and stratified scoring formulae. Experimental results on a benchmark data set validates the effectiveness of the proposed model.