一种产品评论信息的意见挖掘技术研究

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...     

中文情感分析研究综述

近年来,随着互联网在中国的普及,网络上大量出现带有主观性的文本,如用户在博客、微博、等社交网络发表的评论,这些评论信息包含大量情感信息和主观观点.有效挖掘此类文本的信息对于电子商务、信息预测,舆情监控有着重要实用价值.当前,情感分析已经成为自然语言处理学术界的研究热点.     

汉语意见型主观性文本类型体系的研究

主观性文本是一种描述个人想法、情感和意见等的非约束性文本。它与主要描述以事实为主的客观性文本在内容和结构上有很大的不同。意见型文本是包含有意见元素(意见持有者、意见陈述范围、意见主题和意见情感)的一种主观性文本,它大量出现在网上的电子公告板、论坛和博客等媒介中,受到广泛的关注,并成为研究意见挖掘方法和技术的语料。该文介绍了主观性文本的定义及其与客观性文本的差异,同时着重讨论了意见型文本的定义、特点、类型体系及其在意见挖掘技术中的应用。     

领域相关的汉语情感词典扩展

动态情感知识的获取,特别是领域相关极性词典的构建一直是意见挖掘和情感分析系统在开放应用时面临的主要挑战之一。该文面向产品评价文本提出一种汉语情感极性词典扩展方法。该方法首先采用序列标注方法从意见文本中抽取产品意见要素,同时构建属性-评价对;然后,对抽取的属性-评价对进行正规化,以减少词典扩展中的复杂性和噪声;最后,改进PolarityRank算法的构图方式以使其适用于汉语文本,从而完成词典扩展。在汽车和手机两个领域的意见文本的实验结果表明领域相关的情感极性词语的扩展有利于情感极性分类性能的提高。
     

基于集成情感成员模型的文本情感分析方法

文本情感分类是指通过挖掘和分析文本中的观点、意见和看法等主观信息,对文本的情感倾向做出类别判断。基于集成情感成员模型提出一种文本情感分析方法。把基于改进的神经网络、基于语义特征和基于条件随机场的三个情感分类模型作为成员模型集成在一起。集成后的模型能够涵盖不同的情感特征,从而克服了传统集成学习中仅关注成员模型处理结果的不足。以公开语料进行实验,集成模型融合了多个成员模型的优势,分类正确率达到了88.2%,远高于任一成员模型的效果。     

文本情感分析综述

以文本颗粒度为视角,从情感词抽取、语料库和情感词典构建、评价对象与意见持有者分析、篇章级情感分析、实际应用五个方面对文本情感分析文献进行了梳理,并做出必要评述。指出当前情感分析系统的准确率普遍不高,进一步研究的重点在于：自然语言处理的研究成果在文本情感倾向分析中更广泛和贴切的应用;选取文本情感倾向分类的特征和方法;利用现有语言工具和相关资源,规范、快速地构造语言工具和相关资源并应用。     

文本情感分析研究概述

对文本情感分析研究进行总结,从情感词抽取、语料库和情感词典构建、主观分析三个方面对文本情感分析研究相关文献进行梳理、评述,最后介绍了实际应用.     

基于领域情感词典特征表示的细粒度意见挖掘

细粒度意见挖掘的主要目标是从观点文本中获取情感要素并判断情感倾向。现有方法大多基于序列标注模型,但很少利用情感词典资源。该文提出一种基于领域情感词典特征表示的细粒度意见挖掘方法,使用领域情感词典在观点文本上构建特征表示并将其加入序列标注模型的输入部分。首先构建一份新的电商领域情感词典,然后在电商评论文本真实数据上,分别为条件随机场(CRF)和双向长短期记忆-条件随机场(BiLSTM-CRF)这两种常用序列标注模型设计基于领域情感词典的特征表示。实验结果表明,基于电商领域情感词典的特征表示方法在两种模型上都取得了良好的效果,并且超过其他情感词典。     

评价文本中意见分布规律研究

对评价文本的意见挖掘旨在提取由对象、特征、评价语和倾向构成的元组。当前方法主要依赖情感词和语言学启发信息获得主观句表达的意见,忽略了语义表达因素,导致意见召回率较低。该文以实际语料为数据基础,寻找意见表达方式的分布规律。文中明确了判别意见、主观句和客观句的准则,并对ChnSentiCorp语料集中12 000个句子进行了标注。统计结果表明,意见特征类别有限且领域差异大;特征呈随机分布,隐性特征占31.8%;客观句形式意见占36%;在表达方式上有5种主观句式、3种客观句式,意见在各句式上分布不平均。论文最后给出提升意见挖掘性能的5条策略。     

面向产品评论的意见挖掘研究综述

近年来,随着电子商务的快速发展,面向产品评论的意见挖掘研究受到国内外学者的广泛关注,成为学术界的研究热点之一.对产品评论进行意见挖掘,不仅能为用户购物提供决策支持,还可以帮助生产商对产品和服务进行改进,具有重要的研究意义.对面向产品评论的意见挖掘的研究现状进行归纳和总结.首先将该问题分为3个子任务:意见信息抽取、情感分析,意见归纳.然后基于国内外的研究进展对它们进行详细的介绍和分析.并讨论该领域其他一些值得关注的问题.     

The evolution of sentiment analysis—A review of research topics,venues, and top cited papers

Sentiment analysis is one of the fastest growing research areas in computer science, making it challenging to keep track of all the activities in the area. We present a computer-assisted literature review, where we utilize both text mining and qualitative coding, and analyze 6996 papers from Scopus. We find that the roots of sentiment analysis are in the studies on public opinion analysis at the beginning of 20th century and in the text subjectivity analysis performed by the computational linguistics community in 1990’s. However, the outbreak of computer-based sentiment analysis only occurred with the availability of subjective texts on the Web. Consequently, 99% of the papers have been published after 2004. Sentiment analysis papers are scattered to multiple publication venues, and the combined number of papers in the top-15 venues only represent ca. 30% of the papers in total. We present the top-20 cited papers from Google Scholar and Scopus and a taxonomy of research topics. In recent years, sentiment analysis has shifted from analyzing online product reviews to social media texts from Twitter and Facebook. Many topics beyond product reviews like stock markets, elections, disasters, medicine, software engineering and cyberbullying extend the utilization of sentiment analysis.     

一种基于情感倾向分析的网络团体意见领袖识别算法

意见领袖对网络舆情的产生和发展有着重要的指引作用,挖掘和识别网络社区中的意见领袖有重要的现实意义。结合聚类算法和分类算法的优势,提出一种基于话题内容分析的兴趣团体发现方法,以有效识别出兴趣团体。并通过分析用户回帖情感倾向来计算用户间链接的权重。在此基础上,提出了一种新的LeaderRank意见领袖发现算法,通过实验证明该算法能有效提高意见领袖挖掘的准确度。     

Feature-based opinion mining through ontologies

The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public’s opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users’ opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches.     

情感分析与认知

分析了情感分析的3个主要步骤,包括文本情感获取与表达、文本情感分类与计算以及文本情感分析的应用.情感分析得到的结论主要是对相关观点的摘要、对相关事件态度的预测或者统计等,但这些结论都没有发挥文本情感在认知中的作用.为了将情感分析应用于认知科学,提出了情感由情感信号和情感实体组成的观点.情感信号主要是指情感的一些形式载体,比如心跳加速、脸红等这些人体内外的某些表现,表达情感的文字、图片、声音等这类媒体.情感实体主要是指人类对情感形成的一种共识,比如爱、恨、憎恶、高兴、羞愧、嫉妒、内疚、恐惧、焦虑等与人的意识相关联的部分.同时提出了在人工智能中利用情感信息的设想.这对于模拟情感对认知的影响具有一定的意义.     

Mining sentiments in SMS texts for teaching evaluation

This paper explores the potential application of sentiment mining for analyzing short message service (SMS) texts in teaching evaluation. Data preparation involves the reading, parsing and categorization of the SMS texts. Three models were developed: the base model, the “corrected” model which adjusts for spelling errors and the “sentiment” model which extends the “corrected” model by performing sentiment mining. An “interestingness” criterion selects the “sentiment” model from which the sentiments of the students towards the lecture are discerned. Two types of incomplete SMS texts are also identified and the implications of their removal for the analysis ascertained.     

基于句法特征的评价对象抽取方法研究

评价对象抽取是情感分析任务中一个重要的子任务。该文使用基于条件随机场模型的监督学习方法实现英文的评价对象抽取。为了更好的捕捉评价对象和情感词之间的关系,引入句法分析用以加入丰富的句法特征提高评价对象抽取性能。实验中,我们在两个不同的数据集上考查了句法特征对评价对象抽取性能的影响,并做了详细的分析比较。实验结果表明,将句法特征应用在评价对象抽取任务中能够取得不错的效果,明显提高了评价对象的抽取召回率。     

短文本情感分析的研究现状 ——从社交媒体到资源稀缺语言

随着社交网络的逐渐成熟,各类语种的文本出现在社交网络上。而这些非规范的短文本蕴藏着人们对事物的褒贬、需求等意见,是国家政府和企业了解公众舆论的重要参考信息,具有重大的研究价值和应用价值。首先,对 目前互联网短文本情感分析领域常用的神经网络、跨语言和应用语言学知识等研究方法进行归纳和总结;其次,对当前短文本情感分析研究的热点领域——社交媒体和资源稀缺语言的情感分析进行现状分析;最后,对短文本情感分析研究的趋势进行总结,分析存在的问题,并对未来进行展望。     

A novel deterministic approach for aspect-based opinion mining in tourism products reviews

This work proposes an extension of Bing Liu’s aspect-based opinion mining approach in order to apply it to the tourism domain. The extension concerns with the fact that users refer differently to different kinds of products when writing reviews on the Web. Since Liu’s approach is focused on physical product reviews, it could not be directly applied to the tourism domain, which presents features that are not considered by the model. Through a detailed study of on-line tourism product reviews, we found these features and then model them in our extension, proposing the use of new and more complex NLP-based rules for the tasks of subjective and sentiment classification at the aspect-level. We also entail the task of opinion visualization and summarization and propose new methods to help users digest the vast availability of opinions in an easy manner. Our work also included the development of a generic architecture for an aspect-based opinion mining tool, which we then used to create a prototype and analyze opinions from TripAdvisor in the context of the tourism industry in Los Lagos, a Chilean administrative region also known as the Lake District. Results prove that our extension is able to perform better than Liu’s model in the tourism domain, improving both Accuracy and Recall for the tasks of subjective and sentiment classification. Particularly, the approach is very effective in determining the sentiment orientation of opinions, achieving an F-measure of 92% for the task. However, on average, the algorithms were only capable of extracting 35% of the explicit aspect expressions, using a non-extended approach for this task. Finally, results also showed the effectiveness of our design when applied to solving the industry’s specific issues in the Lake District, since almost 80% of the users that used our tool considered that our tool adds valuable information to their business.     

文本意见挖掘综述

意见挖掘是针对主观性文本自动获取有用的意见信息和知识,它是一个新颖而且十分重要的研究课题。这种技术可以应用于现实生活中的许多方面,如电子商务、商业智能、信息监控、民意调查、电子学习、报刊编辑、企业管理等。本文首先对意见挖掘进行了定义,然后阐述了意见挖掘研究的目的,接着从主题的识别、意见持有者的识别、陈述的选择和情感的分析四个方面对意见挖掘的研究现状进行了综述,并介绍了几个成型的系统。此外,我们针对汉语的意见挖掘做了特别的分析。最后对整个领域的研究进行了总结。