用模糊逻辑细分评价强度，精准排名实体口碑。
Review Based Entity Ranking using Fuzzy Logic Algorithmic Approach: Analysis
- 通过模糊逻辑将评价词分为强弱等级
- 结合句法依存分析定位特定属性的评价
- 适合需要细粒度用户评价分析的场景
意见挖掘（即情感分析）旨在分析人们对产品、服务、组织等实体及其属性的观点、情绪和态度。传统全息词典方法未考虑观点强度，如极强负面、强负面、中度负面等。本文提出一种基于模糊逻辑算法的方法，根据评价内容的倾向性和强度对实体进行排名。通过整合与特定产品属性相关的副词、形容词、名词和动词等评价词，利用模糊逻辑将其划分为五个精细等级（极弱、弱、中等、强、极强），并借助句法依存解析识别目标属性词的关系。针对每个属性，提取相关评价词以计算实体在该属性上的得分，实现更精准的实体排序。
原文摘要 · Abstract (English)
Opinion mining, also called sentiment analysis, is the field of study that analyzes people opinions, sentiments, evaluations, appraisals, attitudes, and emotions towards entities such as products, services, organizations, individuals, issues, events, topics, and their attributes. Holistic lexicon-based approach does not consider the strength of each opinion, i.e., whether the opinion is very strongly negative (or positive), strongly negative (or positive), moderate negative (or positive), very weakly negative (or positive) and weakly negative (or positive). In this paper, we propose approach to rank entities based on orientation and strength of the entity reviews and user's queries by classifying them in granularity levels (i.e. very weak, weak, moderate, very strong and strong) by combining opinion words (i.e. adverb, adjective, noun and verb) that are related to aspect of interest of certain product. We shall use fuzzy logic algorithmic approach in order to classify opinion words into different category and syntactic dependency resolution to find relations for desired aspect words. Opinion words related to certain aspects of interest are considered to find the entity score for that aspect in the review.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。