A syntactic approach for aspect based opinion mining

Opinion mining or sentiment analysis is the process of analysing the text about a topic written in a natural language and classify them as positive negative or neutral based on the humans sentiments, emotions, opinions expressed in it. Nowadays, the opinions expressed through reviews are increasing day by day on the web. It is practically impossible to analyse and extract opinions from such huge number of reviews manually. To solve this problem an automated opinion mining approach is needed. This task of automatic opinion mining can be done mainly at three different levels, which are document level, sentence level and aspect level. Most of the previous work is in the field of document or sentence level opinion mining. This paper focus on aspect level opinion mining and propose a new syntactic based approach for it, which uses syntactic dependency, aggregate score of opinion words, SentiWordNet and aspect table together for opinion mining. The experimental work was done on restaurant reviews. The dataset of restaurant reviews was collected from web and tagged manually. The proposed method achieved total accuracy of 78.04% on the annotated test set. The method was also compared with the method, which uses Part-Of-Speech tagger for feature extraction; the obtained results show that the proposed method gives 6% more accuracy than previous one on the annotated test set.