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.
翻译:观点几乎贯穿所有人类活动,并成为行为决策的关键影响因素。当前,随着社交网站的蓬勃发展和电子商务平台数量的激增,网络空间中已积累海量的观点数据。给定一组针对特定实体的评价性陈述(包含观点或情感),观点挖掘旨在提取每个陈述中被评论对象的属性与组成部分,并判定评论的情感极性(正面、负面或中性)。尽管近期观点挖掘领域已涌现大量研究成果,其中部分研究涉及基于评论集或观点集的实体排序,但将观点进行更细粒度的分类后对实体进行排序的研究尚属空白。本文提出一种基于细粒度陈述的观点挖掘方法,该方法通过模糊逻辑推理实现观点解析,并依据解析结果对实体进行排序。