Aspect-based opinion ranking framework for product reviews using a Spearman's rank correlation coefficient method

• Published in: Information sciences Vol. 460-461; pp. 23 - 41
• Main Authors: J, Ashok Kumar, S, Abirami
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01-09-2018
• Subjects: Opinion mining; Opinion ranking; Sentiment analysis; Social media; Spearman's rank correlation; Visualization; Opinion ranking; Sentiment analysis; Visualization; Opinion mining; Spearman's rank correlation; Social media
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2018.05.003

•The products are ranked based on the aspects and their opinions by considering positive and negative ranks.•The aspects and their opinions are visualized using the Harel–Koren Fast Multiscale layout.•Spearman's rank correlation coefficient Method is used for ranking and measuring the relationships between ranks.•Supervised learning methods are employed for aspect-based sentiment classification.
Opinion mining (also called sentiment analysis) is a type of natural language processing for computing people's opinions and emotions. It detects opinions from structured, semi-structured, and unstructured social media contents at different levels, such as the document, word, sentence, and aspect levels. In all these levels except aspect, opinion mining identifies the overall subjectivity or sentiment polarities. An aspect level is described as a part or an attribute of an entity. It exactly describes people's likes and dislikes in social media contents. In this paper, we propose a new framework for ranking products based on aspects. First, the system identifies the aspects of products. Second, the aspects and their opinion words are identified and visualized from the products’ reviews using a Harel–Koren fast multiscale layout. Third, the network visualization is constructed and modeled, and a Spearman's rank correlation coefficient based opinion ranking method is applied to rank the products based on positive and negative ranks. Fourth, the supervised learning methods (Naïve Bayes, Maximum Entropy, and Support Vector Machine) are employed for the aspect-based sentiment classification task. Finally, the performance of the system is measured by the experimental results.