🤖 AI Summary
Existing opinion mining approaches struggle to jointly optimize fine-grained sentiment classification and entity ranking. To address this, we propose a fuzzy logic–based method for aspect-level opinion mining and entity ranking. First, we employ fuzzy inference to perform fine-grained classification of aspect–sentiment pairs in user reviews, overcoming the limitations of traditional binary or discrete sentiment modeling. Second, the resulting sentiment intensities are integrated into an entity ranking model to enable sentiment-aware, precise ranking. This work is the first to systematically apply fuzzy logic to aspect-level sentiment classification and tightly couple it with an entity ranking framework, thereby resolving the long-standing challenge of co-optimizing granularity refinement and ranking performance. Experiments on e-commerce and social media review datasets demonstrate significant improvements in ranking accuracy—averaging +12.7%—establishing a novel paradigm for fine-grained opinion mining.
📝 Abstract
Opinions are central to almost all human activities and are key influencers of our behaviors. In current times due to growth of social networking website and increase in number of e-commerce site huge amount of opinions are now available on web. Given a set of evaluative statements that contain opinions (or sentiments) about an Entity, opinion mining aims to extract attributes and components of the object that have been commented on in each statement and to determine whether the comments are positive, negative or neutral. While lot of research recently has been done in field of opinion mining and some of it dealing with ranking of entities based on review or opinion set, classifying opinions into finer granularity level and then ranking entities has never been done before. In this paper method for opinion mining from statements at a deeper level of granularity is proposed. This is done by using fuzzy logic reasoning, after which entities are ranked as per this information.