基于消费者网络评论情感的产品模糊推理研究
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摘要
目前,网络评论情感分析是一个热门的研究领域。本文旨在从大量文本信息中分析出评论者的态度、情感倾向,并进一步挖掘潜在的实用商务信息。随着电子商务的发展,网络评论已经成为消费者与商家、消费者与消费者之间重要的交互资源。网络评论自然语言文本中蕴含着大量的消费者复杂的认知、情感、意志等信息。在国内从自然语言模糊性角度进行情感分析和推理的研究还比较少,所以本文构建了网络评论文本模糊语料资源,模糊情感词汇本体和模糊情感语料库。在此基础上,基于消费者心理行为理论构建了模糊推理规则库,进行推理研究。
在语义资源构建中,首先定义了模糊化处理方法,用基本情感和评价模糊集对网络评论文本情感和评价词汇的极性和程度进行模糊化处理。通过手工分类和自动获取相结合的方法,对语料资源进行词汇粒度级别的标注。然后定义了修饰词模糊语言算子的处理方法,并提出强调词词典和否定词词典概念,最后提出短语和句子级别情感值计算方法。
基于消费者心理行为理论进行推理的过程中,首先提出推理前件的计算方法,结合顾客对产品属性特征的偏好和模糊运算方法计算得到产品综合评价值和情感值。对于多维细分情感值的计算,则根据情感分类研究和发展的现状以及网络评论中消费者情感的特点,构建了情感分类体系。通过对基本情感模糊集合情感模糊矩阵的运算得出积极情感和消极情感值。在心理行为推理规则库的构建过程中,一方面针对不同消费动机类型的消费者,对不同情感和评价强度的产品进行推荐推理；另一方面,依据感知价值公平与消费者积极情感和消极情感的关系,以情感作为推理前件,推理出消费者感知价值公平度。
实验表明基于消费者情感的网络评论产品的推理方法具有合理性。本文把评论情感分析推理与电子商务相结合,从不同角度进行消费者心理行为推理的探索研究,并具有深入研究的价值。
Sentiment analysis of online reviews is one field that has received more and more interests, and its goals are to analyze reviewer's attitude or sentiment, furthermore to mine potential useful business information. With the development of Electronic Commerce online reviews become the richest interactive resources between consumer and business、consumer and consumer. Natural language texts of online reviews implicit a number of information of consumers'complicated perception、emotion、willingness. However, few researches have focused on sentiment analysis and inference from the perspective of natural language fuzziness. So this paper constructs fuzzy semantic resources of online reviews, fuzzy emotive lexicon ontology and fuzzy emotion corpus. Based on them, using consumer behavior and psychology theory constructs fuzzy inference rules and do inference researches.
About constructing semantic resources, firstly, the paper present the definition of the fuzzification method, using Basic Emotion and Evaluation Fuzzy Set to process the polarity and intensity of lexicon. Through the combination of manual classification and acquiring the intensity automatically the work of corpus tagging is fulfilled of lexicon particle size fraction. Then the definition of Fuzzy Operator of Modifiers is given and the Privative Dictionary and Intensifier Dictionary are constructed. Lastly, calculation method of phrase and sentence emotion value is provided.
On the procedure of inference research based on consumer psychology and behavior theory, first of all, the calculation method of inference antecedent is put forward, combined with the preference weight of product attributes and fuzzy calculation method the product comprehensive evaluation and emotion value is offered. For the calculation of emotion classified multi-dimensionally according to the status of the emotional classification and the features of consumers'emotion of online reviews the classification system is determined. By the Basic Emotion Fuzzy Set and fuzzy matrix positive and negative emotion value are obtained. During the procedure of inference rules bases construction, on the one hand for consumers with different consumption motives recommendation inference of product with different emotion and evaluation intensity is realized; On the other hand, according to the relationship between perception value fairness and consumer positive or negative emotion consumer perception value fairness is inferred.
Experiments showed inference method based on consumers'emotion of online reviews is reasonable. These two methods explore inference by consumer psychology and behavior from diverse point of view.
在语义资源构建中,首先定义了模糊化处理方法,用基本情感和评价模糊集对网络评论文本情感和评价词汇的极性和程度进行模糊化处理。通过手工分类和自动获取相结合的方法,对语料资源进行词汇粒度级别的标注。然后定义了修饰词模糊语言算子的处理方法,并提出强调词词典和否定词词典概念,最后提出短语和句子级别情感值计算方法。
基于消费者心理行为理论进行推理的过程中,首先提出推理前件的计算方法,结合顾客对产品属性特征的偏好和模糊运算方法计算得到产品综合评价值和情感值。对于多维细分情感值的计算,则根据情感分类研究和发展的现状以及网络评论中消费者情感的特点,构建了情感分类体系。通过对基本情感模糊集合情感模糊矩阵的运算得出积极情感和消极情感值。在心理行为推理规则库的构建过程中,一方面针对不同消费动机类型的消费者,对不同情感和评价强度的产品进行推荐推理；另一方面,依据感知价值公平与消费者积极情感和消极情感的关系,以情感作为推理前件,推理出消费者感知价值公平度。
实验表明基于消费者情感的网络评论产品的推理方法具有合理性。本文把评论情感分析推理与电子商务相结合,从不同角度进行消费者心理行为推理的探索研究,并具有深入研究的价值。
Sentiment analysis of online reviews is one field that has received more and more interests, and its goals are to analyze reviewer's attitude or sentiment, furthermore to mine potential useful business information. With the development of Electronic Commerce online reviews become the richest interactive resources between consumer and business、consumer and consumer. Natural language texts of online reviews implicit a number of information of consumers'complicated perception、emotion、willingness. However, few researches have focused on sentiment analysis and inference from the perspective of natural language fuzziness. So this paper constructs fuzzy semantic resources of online reviews, fuzzy emotive lexicon ontology and fuzzy emotion corpus. Based on them, using consumer behavior and psychology theory constructs fuzzy inference rules and do inference researches.
About constructing semantic resources, firstly, the paper present the definition of the fuzzification method, using Basic Emotion and Evaluation Fuzzy Set to process the polarity and intensity of lexicon. Through the combination of manual classification and acquiring the intensity automatically the work of corpus tagging is fulfilled of lexicon particle size fraction. Then the definition of Fuzzy Operator of Modifiers is given and the Privative Dictionary and Intensifier Dictionary are constructed. Lastly, calculation method of phrase and sentence emotion value is provided.
On the procedure of inference research based on consumer psychology and behavior theory, first of all, the calculation method of inference antecedent is put forward, combined with the preference weight of product attributes and fuzzy calculation method the product comprehensive evaluation and emotion value is offered. For the calculation of emotion classified multi-dimensionally according to the status of the emotional classification and the features of consumers'emotion of online reviews the classification system is determined. By the Basic Emotion Fuzzy Set and fuzzy matrix positive and negative emotion value are obtained. During the procedure of inference rules bases construction, on the one hand for consumers with different consumption motives recommendation inference of product with different emotion and evaluation intensity is realized; On the other hand, according to the relationship between perception value fairness and consumer positive or negative emotion consumer perception value fairness is inferred.
Experiments showed inference method based on consumers'emotion of online reviews is reasonable. These two methods explore inference by consumer psychology and behavior from diverse point of view.
引文
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[4]SENECAL S, NANTEL J. The influence of online product recommendations on consumers' online choices [J], Journal of Relating,2004,80(2):159-169.
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[15]娄德成,姚天防.汉语句子语义极性分析和观点抽取方法的研究[J].计算机应用,2006,26(11)：622-2625.
[16]姚天防,聂青阳,李建超等.一个用于汉语汽车评论的意见挖掘系统[C].中文信息处理前沿进展——中国中文信息学会二十五周年学术会议论文集,北京,2006：260-281.
[17]姚天眆,娄德成,汉语语句主题语义倾向分析方法的研究[J],中文信息学报,2007,21(5)：73—79
[18]Miao Q L, Li Q D, Dai R W. AMAZING:A sentiment mining and retrieval system[J]. Expert Systems with Application 2009(39):7192-7198
[19]Malik M S M, Mohand B. Using WordNet's Semantic Relations for Opinion Detection in Blogs[J]. Advances in Information Retrieval.2009:729-733.
[20]杜伟夫,谭松波,云晓春,程学旗.一种新的情感词汇语义倾向计算方法[J].计算机研究与发展.2009,46(10)：1713-1720.
[21]李军辉,王红玲,周国栋等.语义角色标注中句法特征的研究[J].中文信息学报,2009,23(6)：11-18.
[22]朱嫣岚,闵锦,周雅倩等.基于Hownet的词汇语义倾向计算[J].中文信息学报.2006,20(1)：11-12.
[23]PANG B, LEE L. Seeing stars:Exploiting class relationships for sentiment categorization with respect to rating scales [C]. Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics. Morristown, NJ, USA: Association for Computational Linguistics,2005:115-124.
[24]王素格.基于web的评论文本情感分类问题研究[D].上海：上海大学,2008.
[25]EGUCHI K, LAVRENKO V. Sentiment retrieval using generative models[C]. Proceedings of the 2006 Conference on Empirical Methods for Computational Linguistics, 2006:345-354.
[26]MISHNE G, GLANCE N. Predicting moive sales from blogger sentiment[C]. Proceedings of the 21st National Conference on Artificial Intelligence. Menlo Park, California:AAAI Press,2006:155-158.
[27]MISHNE G, RIJKE DE M. MoodViews:Tools for blog mood analysis[C]. Proceedings of the 21st National Conference on Artificial Intelligence. Menlo Park, California:AAAI Press,2006:153-154.
[28]RUDY P, MIKE T. Sentiment analysis:A combined approach [J]. Journal of Informetrics. 2009(3):143-157.
[29]LI N, WU D D. Using text mining and sentiment an analysis for online forums hotspot detection and forecast[J]. Decision Support Systems 2010(48):354-386.
[30]JINDAL N, LIU B. Identifying comparative sentences in text document [C]. Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York:ACM Press,2006:244-251.
[31]ZADEH L A. Fuzzy sets,Information and Control[M], vol.8,1965:183-190.
[32]李柏年.模糊数学及其应用[M].2007：1-2.
[33]TIMOTHY J. ROSS.钱同惠等译.模糊逻辑及其工程应用[M].北京：电子工业出版社,2001.
[34]谢维信.工程模糊数学方法[M].西安：西安电子科技大学出版社,1991,12：70-72.
[35]张德丰.Matlab模糊系统设计[M].北京：国防工业出版社,2009,4：36-37
[36]吴晓莉,林哲辉.MATLAB辅助模糊系统设计[M].西安：西安电子科技大学出版社,2002,6：50-54.
[37]董振东等.知网.http://www. keenage. com/html/e_index. html
[38]EKMAN P. Facial expression and emotion[M]. American Psychologist.1993 48(4):384-392.
[39]Zhang Y, Li Z M, Ren F J. et al. Semi-automatic emotion recognition from textual input based on the constructed emotion thesaurus[C]. International Conference on [30] [40] Natural Language Processing and Knowledge Engineering. Wuhan,2005: 571-576.
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