共查询到19条相似文献,搜索用时 140 毫秒
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存在于网上商城的大量的产品评论数量在以惊人的速度增长,并成为文本挖掘研究的一个新兴热点.由于中英文语言本身的不同,我们需要将汉语评论意见挖掘作为一个单独的领域来研究.在前人研究的基础上介绍了一种新的情感分类方法,第一次提出了将主观性意见语句分为以下三类:强极性主观性意见语句,依赖上下文语境的弱极性主观性意见语句,第三类... 相似文献
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目前的情感分析研究大部分仅局限于能够明显地表达意见的主观性文本,却没有对一些隐含地表达情感的文本进行分析.针对这一不足,提出一种基于条件随机场(CRFs)模型的意见挖掘中维吾尔语文本隐式情感分析方法.利用互信息(MI)衡量上下文的依赖度,结合词法、语境依赖词、标点符号和习语等特征用于隐式情感分析.在特征选择时,通过对信息增益(IG)进行改进,解决语料中数据集不平衡的问题.该方法用于维吾尔语文本隐式情感分析的准确率为77.11%,召回率为78.37%,表明了其在意见挖掘中隐式情感分析任务上的有效性. 相似文献
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传统的情感分析研究通过分析, 确定词语、句子或篇章的情感, 但忽略了情感表达的主题。针对这一不足, 该文提出了一种基于双层CRFs模型的细粒度意见挖掘中维吾尔语意见型文本陈述级情感分析方法。第一层模型识别意见型文本中的主题词和意见词, 确定意见陈述的范围, 并将识别结果传递给第二层模型, 将其作为重要特征之一, 用于陈述级情感分析。细粒度意见挖掘中情感分析的目标是构建<意见陈述, 主题词, 意见词, 情感>四元组。该方法用于维吾尔语陈述级情感分析的准确率为77.41%, 召回率为78.51%, 证明了该方法在细粒度意见挖掘中情感分析任务上的有效性。 相似文献
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近年来随着计算机、人工智能、心理学等学科交叉领域的不断延伸,情感分析引起了很多研究人员的兴趣。情感分析主要是对主观性文本进行挖掘与分析,从中获取有价值的信息。本文针对中文文本情感分析的研究现状与进展进行总结。首先介绍文本情感分析的内容,并按粒度层次,从词语级、语句级介绍相关的技术,分析了近年来的一些研究进展。接着介绍了中文文本情感分析的方法,最后总结了中文文本情感分析的研究难点与未来的研究方向。 相似文献
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文本情感倾向分析是意见挖掘和情感文摘中的一个重要环节,而在情感倾向分析中涉及到的是主观性文本,这就需要进行主客观文本分类。当前的主客观文本分类方法主要是基于特征词典的概率统计方法,并没有考虑特征之间的语法与语义关系。针对该问题,该文提出一种基于隐马尔可夫模型(HMM)的主观句识别方法。该方法首先从训练语料中抽取具有明显分类效果的七类主客观特征,然后每个句子应用HMM进行特征角色类别标注,并依据标注的结果计算句子的权重,最终识别主观句。该方法在第六届中文倾向性分析评测任务中能够有效地识别主观句。 相似文献
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态度挖掘是近年来文本挖掘领域的热点课题之一,旨在发现文本中作者的主观态度倾向,为基于舆情的决策过程提供支持。目前已有的态度挖掘算法绝大多数都基于情感词典来识别情感词,在此基础上判别句子或文本的总体态度倾向。然而,手工构造和维护一部完善的情感词典是不现实的。对中文情感词的极性判别问题进行了研究,提出了基于直推式学习的中文情感词极性判别算法。该算法以少量情感词为种子,利用词典中词汇的解释信息,直推出其他词的情感极性。与使用相同情感种子词的解释信息作为训练数据的有监督学习算法相比,直推式学习算法的识别精度提高了20%左右。 相似文献
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While data mining is well established in practice, opinion mining is still in its infancy, with issues in particular around the development of methodologies which effectively extract accurate, reliable, influential and useful information from the raw opinion data collected from informal product reviews. Current approaches adopt a single-variable approach, focusing on individual metrics—word length, the presence of keywords, or the overall semantic orientation of terms within the data—while neglecting to evaluate whether these individual artifacts are indicative of the tone of a given review. This approach has significant limitations when we move from trying to merely evaluate whether an online opinion is positive or negative, to trying to evaluate how likely it is that the opinion will influence others. Given this issue, one promising avenue would be to evaluate the general analysis approaches utilized by opinion mining algorithms and identified in the literature in terms of how accurately they reflect how people actually interpret and are influenced by electronic online reviews. Through interviewing and a follow up survey of 136?participants, the validity of the approach in terms of ascertaining the tone of a piece of text can be evaluated, as well as the identification of measurable factors within text which make a given opinionated text more or less influential in an online context, further facilitating the development of more effective multivariate opinion mining approaches. Furthermore, the identification of factors which make an online opinion text more or less persuasive helps to facilitate the development of opinion mining approaches which can evaluate how likely a review is to affect an individual’s decision making. 相似文献
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互联网上的用户生成内容UGC(User Generated Content)中蕴含的用户主观观点信息对分析用户行为、用户需求等工作有着重要的价值。设计一套基于自然语言理解的互联网UGC文本主观观点分析系统WSAM,该系统能挖掘出用户主观观点所蕴含的关注对象和主观成分。分析了互联网UGC现象和生成原因,总结出UGC中用户主观观点中的四种主要类型。挖掘用户主观观点过程中,将用户主观观点的挖掘转化为句子中主观观点关注对象的识别和主观成分的判断。算法结合基于词语类、结构类等相关特征,采用最大熵分类器挖掘用户主观观点。实验验证,WSAM系统所采用的算法性能较好,且还能够灵活扩充出情感分析(Opin-ion Mining)等相关应用,同样也能达到较好的结果。 相似文献
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Mujtaba HUSNAIN Malik Muhammad Saad MISSEN Nadeem AKHTAR Micka l COUSTATY Shahzad MUMTAZ V. B. Surya PRASATH 《Frontiers of Computer Science》2021,15(4):154614
Sentiment lexicons (SL) (aka lexical resources) are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms. These lexicons play an important role in performing several different opinion mining tasks. The efficacy of the lexicon-based approaches in performing opinion mining (OM) tasks solely depends on selecting an appropriate opinion lexicon to analyze the text. Therefore, one has to explore the available sentiment lexicons and then select the most suitable resource. Among available resources, SentiWordNet (SWN) is the most widely used lexicon to perform tasks related to opinion mining. In SWN, each synset of WordNet is being assigned the three sentiment numerical scores; positive, negative and objective that are calculated using by a set of classifiers. In this paper, a detailed and comprehensive review of the work related to opinion mining using SentiWordNet is provided in a very distinctive way. This survey will be useful for the researchers contributing to the field of opinion mining. Following features make our contribution worthwhile and unique among the reviews of similar kind: (i) our review classifies the existing literature with respect to opinion mining tasks and subtasks (ii) it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels (word, sentences, document, aspect, clause, and concept levels) (iii) this state-ofart review covers each article in the following dimensions: the designated task performed, granularity level of the task completed, results obtained, and feature dimensions, and (iv) lastly it concludes the summary of the related articles according to the granularity levels, publishing years, related tasks (or subtasks), and types of classifiers used. In the end, major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed. 相似文献
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随着互联网的不断发展,网络上的文本数据日益增多,如果能对这些数据进行有效分类,那么更有利于从中挖掘出有价值的信息,因此文本数据的管理和整合显得十分重要。文本分类是自然语言处理任务中的一项基础性工作,主要应用于舆情检测及新闻文本分类等领域,目的是对文本资源进行整理和归类。基于深度学习的文本分类,在对文本数据处理中,表现出较好的分类效果。本文对用于文本分类的深度学习算法进行详细阐述,按照深度学习的不同算法进行分类,并分析各种算法的特点,最后对深度学习算法在文本分类领域的未来研究方向进行总结。 相似文献
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Enric Junqué de Fortuny Tom De Smedt David Martens Walter Daelemans 《Expert systems with applications》2012,39(14):11616-11622
At the year end of 2011 Belgium formed a government, after a world record breaking period of 541 days of negotiations. We have gathered and analysed 68,000 related on-line news articles published in 2011 in Flemish newspapers. These articles were analysed by a custom-built expert system. The results of our text mining analyses show interesting differences in media coverage and votes for several political parties and politicians. With opinion mining, we are able to automatically detect the sentiment of each article, thereby allowing to visualise how the tone of reporting evolved throughout the year, on a party, politician and newspaper level. Our suggested framework introduces a generic text mining approach to analyse media coverage on political issues, including a set of methodological guidelines, evaluation metrics, as well as open source opinion mining tools. Since all analyses are based on automated text mining algorithms, an objective overview of the manner of reporting is provided. The analysis shows peaks of positive and negative sentiments during key moments in the negotiation process. 相似文献