Aspect-Based Sentiment Analysis Using a Hybridized Approach Based on CNN and GA

Abstract

Sentiment analysis is a computational analysis of unstructured textual data, used to assess the person's attitude from a piece of text. Aspect-based sentimental analysis defines the relationship among opinion targets of a document and the polarity values corresponding to them. Since aspects are often implicit, it is an extremely challenging task to spot them and calculate their respective polarity. In recent years, several methods, strategies and improvements have been suggested to address these problems at various levels, including corpus or lexicon-based approaches, term frequency and reverse document frequency approaches. These strategies are quite effective when aspects are correlated with predefined groups and may struggle when low-frequency aspects are involved. In terms of accuracy, heuristic approaches are stronger than frequency and lexicon based approaches, however, they consume time due to different combinations of features. This article presents an effective method to analyze the sentiments by integrating three operations: (a) Mining semantic features (b) Transformation of extracted corpus using Word2vec (c) Implementation of CNN for the mining of opinion. The hyperparameters of CNN are tuned with Genetic Algorithm (GA). Experimental results revealed that the proposed technique gave better results than the state-of-the-art techniques with 95.5% accuracy rate, 94.3% precision rate, 91.1% recall and 96.0% f-measure rate.