Personalized ranking of products using aspect-based sentiment analysis and Plithogenic sets

The availability of the content on the web has increased enormously in the last decade. Many reviews are written by the users on the e-commerce websites for the products they buy. These reviews are read by customers who are interested in buying those products. Sometimes, these reviews are in thousands which makes it difficult to read them. Customers also want to search reviews based on their preferred aspects to make a buying decision. In this paper, a novel approach for Multi-Criteria Decision Making (MCDM) for multi-aspect based personalized ranking of the products is proposed. It characteristically uses customer preferences as one of the inputs for decision-making. Opinions on various aspects are extracted using Aspect-Based Sentiment Analysis (ABSA) which becomes the second input to the framework which uses Plithogenic sets. This model uniquely incorporating varying customer preferences by mapping them to plithogenic degree of contradictions and modelling linguistic uncertainties in online reviews to create a personalized ranking of products using plithogenic aggregation. It has been shown empirically that our approach outperforms the existing MCDM approaches namely TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and WSM (Weighted Sum Model) and some of the state-of-the-art methods.