Abstract
The aspect-based online opinions expressed by users on social media sites have become a popular source of information for consumers regarding their purchase decisions as well as for companies seeking opinions on their products. Therefore, it is important to develop aspect-based opinion mining applications with an emphasis on extracting and classifying the aspect-based opinions expressed by users about products in a given review. Previous studies have used a limited set of heuristic patterns for aspect extraction with both supervised (annotated-dataset-based) and unsupervised (lexical-resource-based) aspect-related sentiment classification algorithms. However, the present study proposes an integrated framework comprising of an extended set of heuristic patterns for aspect extraction, a hybrid sentiment classification module with the additional support of intensifiers and negations, and a summary generator. The performance evaluation of the proposed aspect-based opinion mining system using state-of-the-art methods shows that the proposed system outperforms the alternative methods in terms of better precision, recall and F-measure, since it achieves an average precision of 85%, an average recall of 73% and an average F-measure of 0.78. The comparative results indicate that the proposed technique provides more efficient results for the aspect-sentiment extraction, classification and summary generation of online product reviews.