用模糊逻辑提升情感分析粒度，实现更精准的实体排序
Opinion Mining Based Entity Ranking using Fuzzy Logic Algorithmic Approach
- 通过模糊逻辑对评价语句进行细粒度情感分析
- 基于深层情感信息对实体进行排序，提升准确性
- 适合需要精细化用户反馈分析的电商与社交平台
意见是人类活动的核心，深刻影响行为决策。随着社交媒体和电子商务网站的兴起，网络上积累了海量意见数据。针对包含对特定实体评价的陈述，意见挖掘旨在提取每条语句中被评论的属性与成分，并判断其情感倾向（正面、负面或中性）。尽管近年来已有大量关于意见挖掘及基于评论集的实体排名研究，但将意见分类至更细粒度层级后进行实体排序的方法尚未出现。本文提出一种基于模糊逻辑推理的意见挖掘方法，实现对评价语句的深层次情感解析，进而依据该分析结果对实体进行排序。
原文摘要 · Abstract (English)
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.
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