Abstract
The plethora of online reviews available on the internet has great insights about various products or entities. However, selecting or recommending a product by sifting through these reviews is a tedious and time consuming task. Sentiment analysis of such reviews helps understand the pros or cons of the product. In recent years researchers and industry people have leveraged such reviews to rank or recommend the products to new users. Ranking or recommending products using text reviews is a challenging task. In this study, we propose a framework to get a ranking of products using aspect-based sentiment analysis (ABSA). ABSA is a fine-grained sentiment analysis task that is useful for extracting opinions about the different aspects of a product. To facilitate automated ABSA, a novel approach for aspect sentiment classification is proposed in this study. Further to rank the products based on aspect-specific performance, a framework is developed using Multi-criteria decision-making (MCDM) techniques. A case study is performed in the automobile domain to rank various cars using the ABSA approach. To validate the effectiveness of the proposed approach, the ranking results are compared with the numeric ratings available for various aspects of the car. The experimental results show that the ranking obtained by the proposed approach matches the numerical ratings. Therefore, the car selection problem or recommendation of cars in the automobile domain can be effectively handled using the proposed framework.
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