Product aspect ranking using sentiment analysis and TOPSIS

The explosive growth of customer reviews on e-commerce websites has inspired many researchers to explore the problem of identifying the product aspects that have been mentioned in online reviews. Most of the conducted research studies extract the product aspects based on three main criteria: 1) extracting the aspects that have been commented repeatedly in online reviews, 2) determining of important aspects as those that have been described positively and negatively by many customers in their reviews, and 3) the association between the domain product aspect (like `camera') and the other aspects contained in a specific product review. However, a lacuna remains as how to efficiently investigate online reviews to identify the most important product aspects by considering all the three criteria jointly. In response, this paper proposes a novel product aspect ranking framework using sentiment analysis and TOPSIS (Technique for Order Performance by Similarity to Ideal Solution). The proposed work is decomposed into two stages: aspect extraction and aspect ranking. In aspect extraction stage, sentiment analysis is used to identify the product aspects from customer reviews in an unsupervised manner based on the three criteria of extraction. In the second stage, the extracted product aspects from the previous criteria have been involved simultaneously in TOPSIS to produce a ranked list of the most representative product aspects. The empirical evaluation of the proposed work using online reviews of four products shows its effectiveness in finding representative aspects.