Aspect-based sentiment analysis using adaptive aspect-based lexicons

Abstract

Reviews expressed in e-commerce websites have formed an important source of information for both consumers and enterprises. Text sentiment analysis approaches aim to detect the sentiments of written reviews in order to achieve a better understanding of public opinion towards entities. Aspect-based sentiment analysis deals with capturing sentiments expressed towards each aspect of entities. A common approach in sentiment analysis problems is to take advantage of lexicons to generate features for classification of reviews. Existing aspect-based approaches fail to properly adapt general lexicons to the context of aspect-based datasets which results in reduced performance. To address this problem, this paper proposes extensions of two lexicon generation methods for aspect-based problems; one using statistical methods, and another using a genetic algorithm presented in our previous works. The aforementioned lexicons are then fused with prominent static lexicons to classify the aspects in reviews; this outperforms our previous works according to the t-test results (with a p-value of less than 0.001). Experimental results indicate that the proposed approach outperforms baseline methods in aspect-based polarity classification on Bing Liu's customer review datasets and improves precision, recall and F-measure by 6.0, 1.0, and 7.4 percentage points respectively.