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FLORA: a hierarchical fuzzy system for online accommodation review analysis

机译：FLORA：在线住宿评论分析的分层模糊系统

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摘要

Purpose - This paper aims to introduce a hierarchical fuzzy system for an online review analysis named FLORA. FLORA enables tourists to decide their destination without reading numerous reviews from experienced tourists. It summarizes reviews and visualizes them through a hierarchical structure. The visualization does not only present overall quality of an accommodation, but it also presents the condition of the bed, hospitality of the front desk receptionist and much more in a snap. Design/methodology/approach - FLORA is a complete system which acquires online reviews, analyzes sentiments, computes feature scores and summarizes results in a hierarchical view. FLORA is designed to use an overall score, rated by real tourists as a baseline for accuracy comparison. The accuracy of FLORA has achieved by a novel sentiment analysis process (as part of a knowledge acquisition engine) based on semantic analysis and a novel rating technique, called hierarchical fuzzy calculation, in the knowledge inference engine. Findings - The performance comparison of FLORA against related work has been assessed in two aspects. The first aspect focuses on review analysis with binary format representation. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, is achieved with the highest values in precision, recall and F-measure. The second aspect looks at review analysis with a five-point rating scale rating by comparing with one of the most advanced research methods, called fuzzy domain ontology. The results reveal that the hierarchical fuzzy method, with probability weighting of FLORA, returns the closest results to the tourist-defined rating. Research limitations/implications - This research advances knowledge of online review analysis by contributing a novel sentiment analysis process and a novel rating technique. The FLORA system has two limitations. First, the reviews are based on individual expression, which is an arbitrary distinction and not always grammatically correct. Consequently, some opinions may not be extracted because the context free grammar rules are insufficient. Second, natural languages evolve and diversify all the time. Many emerging words or phrases, including idioms, proverbs and slang, are often used in online reviews. Thus, those words or phrases need to be manually updated in the knowledge base. Practical implications - This research contributes to the tourism business and assists travelers by introducing comprehensive and easy to understand information about each accommodation to travelers. Although the FLORA system was originally designed and tested with accommodation reviews, it can also be used with reviews of any products or services by updating data in the knowledge base. Thus, businesses, which have online reviews for their products or services, can benefit from the FLORA system. Originality/value - This research proposes a FLORA system which analyzes sentiments from online reviews, computes feature scores and summarizes results in a hierarchical view. Moreover, this work is able to use the overall score, rated by real tourists, as a baseline for accuracy comparison. The main theoretical implication is a novel sentiment analysis process based on semantic analysis and a novel rating technique called hierarchical fuzzy calculation.
机译：目的-本文旨在为在线评论分析引入名为FLORA的分层模糊系统。 FLORA使游客无需阅读经验丰富的游客的大量评论即可决定目的地。它总结了评论并通过层次结构将其可视化。可视化不仅显示了住宿的整体质量，而且还显示了床的状况，前台接待员的款待以及其他更多信息。设计/方法/方法-FLORA是一个完整的系统，可获取在线评论，分析情绪，计算功能评分并以分层视图的形式汇总结果。 FLORA旨在使用总得分（由真实游客评定）作为基准进行准确性比较。 FLORA的准确性是通过基于语义分析的新颖情感分析过程（作为知识获取引擎的一部分）和知识推理引擎中一种称为分级模糊计算的新颖评分技术来实现的。调查结果-从两个方面评估了FLORA与相关工作的性能比较。第一个方面着重于以二进制格式表示的审阅分析。结果表明，在精度，召回率和F测度方面，具有FLORA概率加权的分层模糊方法是实现的。第二个方面通过与最先进的研究方法之一（称为模糊域本体）进行比较，以五点等级量表评估评论分析。结果表明，采用FLORA概率加权的层次模糊方法将最接近结果的结果定义为游客定义的等级。研究局限性/含义-该研究通过提供新颖的情感分析过程和新颖的评分技术来提高在线评论分析的知识。 FLORA系统有两个局限性。首先，评论是基于个体表达的，这是一个任意的区别，并且在语法上并不总是正确的。因此，由于上下文无关的语法规则不足，因此可能无法提取某些意见。第二，自然语言一直在发展并多样化。在线评论中经常使用许多新兴单词或短语，包括成语，谚语和语。因此，那些单词或短语需要在知识库中手动更新。实际意义-这项研究为旅游业做出了贡献，并向旅行者介绍了关于每种住宿的全面且易于理解的信息，从而为旅行者提供了帮助。尽管FLORA系统最初是根据住宿评论设计和测试的，但也可以通过更新知识库中的数据将其与任何产品或服务的评论一起使用。因此，对其产品或服务具有在线评论的企业可以从FLORA系统中受益。原创性/价值-这项研究提出了一种FLORA系统，该系统可以分析在线评论中的情绪，计算特征分数并以分层视图的形式汇总结果。此外，这项工作能够将真实游客评分的总体得分用作准确性比较的基准。主要的理论含义是基于语义分析的新颖情感分析过程和称为分级模糊计算的新颖评级技术。

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