教材在线评论的情感倾向性分析

为了充分挖掘和应用电子商务网站中的教材评论信息,运用细粒度的情感分类算法对用户的在线评论进行分析,基于教材特征级的情感分析结果,辅助潜在客户和商家做出合理有效的决策.本文首先使用爬虫采集教材的在线评论文本,对其进行去噪、分词和词性标注等预处理;然后分析产品特征,在通用情感词典的基础上扩建领域情感词典;最后基于句法分析结果,结合教材评论的语言特性,设计适合教材评论的情感倾向性分析算法,并通过实验验证了算法的有效性.     

基于高效Transformer的中文在线课程评论方面情感分析

准确挖掘在线课程评论中蕴涵的情感信息对在线课程的健康发展极具价值.现有中文在线课程评论情感分析研究大多为分析整条评论句子情感极性的粗粒度模型,无法准确表达课程评论句子中各个方面的细粒度情感.为此,提出一种基于高效Transformer的中文在线课程评论方面情感分析模型.首先,通过ALBERT预训练模型获得评论文本方面和上下文的动态字向量编码;然后,采用可以并行输入字向量的高效Transformer分别对课程评论文本的方面和上下文进行语义表征;最后,使用交互注意机制交互地学习课程评论文本中方面和上下文的重要部分,并输入方面和上下文的最终表示到情感分类层进行在线课程评论情感极性预测.在中国MOOC网真实数据集上的实验结果表明,高效Transformer中文在线课程评论方面情感分析模型与基线模型相比,在更低的时间开销下准确率达到了80％以上.     

细粒度情感分析的酒店评论研究

酒店在线评论细粒度挖掘具有重要研究意义.以酒店在线评论具体特征属性和情感分类为研究目标,应用Apfiori算法和情感词典匹配算法,对重庆雾都宾馆在线评论数据深入挖掘,挖掘出用户最关注的酒店十大特征和满意度结果,进一步挖掘出商务出差等五种不同出游类型人最关注的酒店五大特征和满意度结果.这种方法不仅能对酒店领域评论进行分析,同样能够应用于其他领域.     

基于粗糙集的在线评论情感分析模型

针对在线评论情感分析的复杂特征抽取问题,提出一种基于粗糙集的在线评论情感分析模型。分析传统词袋性特征,指出固定搭配特征在情感极性判别中的作用,采用粗糙集方法挖掘在线评论中的固定搭配特征,将其融合于SVM与Naive Bayes等情感分析模型中。实际酒店的在线评论情感分析结果表明,增加粗规则后,SVM模型与Naive Bayes模型获得的评论情感判别精度都有所提高。     

基于多特征微博话题情感倾向性判定算法研究

社交网络舆情分析是一种新的研究趋势,而其中微博话题的情感倾向性判定是社交网络舆情分析中的热点。针对微博内容特征以及微博间转发、评论关系特征,构建情感分析用词典、网络用语词典以及表情符号库,设计基于短语路径的微博话题情感倾向性判定算法,以及基于多特征的微博话题情感倾向性判定算法,并进一步利用微博间的转发和评论关系对基于多特征的微博话题情感倾向性判定算法进行优化,其微平均正确率与F值分别达到85.3%和79.4%。     

基于CiteSpace的中文评论文本研究现状与趋势分析

自然语言处理一直是人工智能领域中的热点话题,其中基于评论的文本分析吸引了学者的注意.通过对国内关于评论文本分析的文献进行可视化分析,进而掌握该领域的研究现状和前沿发展趋势.以中国知网为数据来源,共选取453篇有效的核心期刊论文,使用CiteSpace软件绘制知识图谱并加以分析.分析结果显示:该领域的文献数量在近15年内整体呈上升趋势;作者之间、研究机构之间的合作关系并不紧密,尚未形成具有凝聚力的研究群体;情感分析、在线评论、深度学习是目前研究的主要热点.从初期的理论基础发展以及应用方向上的扩展,到后期在分析手段和模型上做出改进,学者们对该领域的研究逐渐深入.未来各研究者及研究机构之间的合作关系还需加强,以深度学习为代表的各类模型未来将持续发展和改善.     

基于SVM和LSTM的在线剧评分析模型

海量的观影评论包含着广大观众对影视作品各方面的偏好,可以为影视剧的拍摄和宣传提供决策支持。提出影视剧在线评论分析模型：利用Python爬取传媒平台的评论信息,经过数据预处理和分词,分别采用SVM方法和LSTM方法对短评文本和长评文本进行情感极性分析,运用统计和可视化方法研究评论词语、语义网络关系、情感倾向演化、文本内容特征和地域热度分布。以公安剧《狂飙》为例进行实证分析,结果表明所提模型可以合理揭示总体情感热度演变规律,发现观众发表评论的内容偏好、行为规律和地域特征。     

基于激活-池化增强BERT模型的在线课程评论情感分析

分析在线课程评论中蕴含的情感对理解学习者状态变化、改进课程质量具有重要意义.依据课程评论的特征,提出一种激活-池化增强的BERT情感分析模型.构建BERT情感分析预训练模型来编码评论文本中分句内词语上下文语义和分句间逻辑关系;设计激活函数层和最大-平均池化层解决BERT模型在课程评论情感分析中存在的过拟合问题;通过新增的情感分类层对在线课程评论进行情感正负极性分类.实验结果表明,激活-池化增强的BERT模型准确率和AUC值与原始BERT模型相比分别提升了约5.5％和5.8％.     

基于加权融合字词向量的中文在线评论情感分析

随着社交网络平台的广泛使用,涌现出大量蕴涵丰富情感信息的在线评论文本,分析评论中表达的情感对企业、平台等具有重要意义。为了解决目前针对在线评论短文本情感分析中存在特征提取能力弱以及忽略短文本本身情感信息的问题,提出一种基于文本情感值加权融合字词向量表示的模型——SVW-BERT模型。首先,基于字、词级别向量融合表示文本向量,最大程度获取语义表征,同时考虑副词、否定词、感叹句及疑问句对文本情感的影响,通过权值计算得到文本的情感值,构建情感值加权融合字词向量的中文短文本情感分析模型。通过网络平台在线评论数据集对模型的可行性和优越性进行验证。实验结果表明,字词向量融合特征提取语义的能力更强,同时情感值加权句向量考虑了文本本身蕴涵的情感信息,达到了提升情感分类能力的效果。     

基于Java实现网络舆情分析系统的研究与实现

通过对各大门户网站、论坛和贴吧的留言和评论的爬取,录入后台数据库。用户可根据主题、内容进行搜索查看。通过利用中科院分词算法进行实现对爬去下来的内容进行分词处理,分词处理后的结果利用自行研究出来的基于权值算法实现的中文情感分析进行评论的倾向性分析,通过对句子结构和主张词以及情感副词的判断来对评论的情感倾向性做出有效地判断,通过情感权值计算后可给出评论的倾向性以供用户查阅和进行其他相关工作。     

Text feature selection for sentiment classification of Chinese online reviews

In order to meet the requirement of customised services for online communities, sentiment classification of online reviews has been applied to study the unstructured reviews so as to identify users’ opinions on certain products. The purpose of this article is to select features for sentiment classification of Chinese online reviews with techniques well performed in traditional text classification. First, adjectives, adverbs and verbs are identified as the potential text features containing sentiment information. Then, four statistical feature selection methods, such as document frequency (DF), information gain (IG), chi-squared statistic (CHI) and mutual information (MI), are adopted to select features. After that, the Boolean weighting method is applied to set feature weights and construct a vector space model. Finally, a support vector machine (SVM) classifier is employed to predict the sentiment polarity of online reviews. Comparative experiments are conducted based on hotel online reviews in Chinese. The results indicate that the highest accuracy of the sentiment classification of Chinese online reviews is achieved by taking adjectives, adverbs and verbs together as the feature. Besides that, different feature selection methods make distinct performances on sentiment classification, as DF performs the best, CHI follows and IG ranks the last, whereas MI is not suitable for sentiment classification of Chinese online reviews. This conclusion will be helpful to improve the accuracy of sentiment classification and be useful for further research.     

基于情感词抽取与LDA特征表示的情感分析方法

情感分析作为文本挖掘的一个新型领域,可用于分类、归纳用户发布的产品评论,从而有助于商家改善服务,提高产品质量;同时为其他消费者提供购买决策。本文提出一种基于情感词抽取与LDA特征表示的情感分析方法,对产品评论进行褒贬二元分类。在情感词抽取中,采用人工构造的情感词典对预处理之后的文本抽取情感词;用LDA模型建立文档的主题分布,以评论-主题分布作为特征,用SVM分类器进行分类。实验结果表明,本文方法在评论褒贬分类方面有着良好的效果。     

Multilingual sentiment analysis: from formal to informal and scarce resource languages

The ability to analyse online user-generated content related to sentiments (e.g., thoughts and opinions) on products or policies has become a de-facto skillset for many companies and organisations. Besides the challenge of understanding formal textual content, it is also necessary to take into consideration the informal and mixed linguistic nature of online social media languages, which are often coupled with localised slang as a way to express ‘true’ feelings. Due to the multilingual nature of social media data, analysis based on a single official language may carry the risk of not capturing the overall sentiment of online content. While efforts have been made to understand multilingual sentiment analysis based on a range of informal languages, no significant electronic resource has been built for these localised languages. This paper reviews the various current approaches and tools used for multilingual sentiment analysis, identifies challenges along this line of research, and provides several recommendations including a framework that is particularly applicable for dealing with scarce resource languages.     

EOSentiMiner: an opinion-aware system based on emotion ontology for sentiment analysis of Chinese online reviews

With the growing availability and popularity of online reviews, consumers' opinions towards certain products or services are generated and spread over the Internet; sentiment analysis thus arises in response to the requirement of opinion seekers. Most prior studies are concerned with statistics-based methods for sentiment classification. These methods, however, suffer from weak comprehension of text-based messages at semantic level, thus resulting in low accuracy. We propose an ontology-based opinion-aware framework – EOSentiMiner – to conduct sentiment analysis for Chinese online reviews from a semantic perspective. The emotion space model is employed to express emotions of reviews in the EOSentiMiner, where sentiment words are classified into two types: emotional words and evaluation words. Furthermore, the former contains eight emotional classes, and the latter is divided into two opinion evaluation classes. An emotion ontology model is then built based on HowNet to express emotion in a fuzzy way. Based on emotion ontology, we evaluate some factors possibly affecting sentiment classification including features of products (services), emotion polarity and intensity, degree words, negative words, rhetoric and punctuation. Finally, sentiment calculation based on emotion ontology is proposed from sentence level to document level. We conduct experiments by using the data from online reviews of cellphone and wedding photography. The result shows the EOSentiMiner outperforms baseline methods in term of accuracy. We also find that emotion expression forms and connection relationship vary across different domains of review corpora.     

Riding the tide of sentiment change: sentiment analysis with evolving online reviews

The last decade has seen a rapid growth in the volume of online reviews. A great deal of research has been done in the area of opinion mining, aiming at analyzing the sentiments expressed in those reviews towards products and services. Most of the such work focuses on mining opinions from a collection of reviews posted during a particular period, and does not consider the change in sentiments when the collection of reviews evolve over time. In this paper, we fill in this gap, and study the problem of developing adaptive sentiment analysis models for online reviews. Given the success of latent semantic modeling techniques, we propose two adaptive methods to capture the evolving sentiments. As a case study, we also investigate the possibility of using the extracted adaptive patterns for sales prediction. Our proposal is evaluated on an IMDB dataset consisting of reviews of selected movies and their box office revenues. Experimental results show that the adaptive methods can capture sentiment changes arising from newly available reviews, which helps greatly improve the prediction accuracy.     

基于语义规则与RNN模型的在线评论情感分类研究

为提高互联网中在线评论文本的情感倾向分类准确率,方便消费者和商家准确高效地获取信息,该文提出一种将语义规则方法与深度学习方法相结合的在线评论文本情感分类模型,对基于情感词典的语义规则信息进行扩展,嵌入到常用特征模板中组合成更有效的混合特征模板;采用Fisher判别准则方法对混合特征模板进行降维以消除特征间的信息冗余;深度学习模型采用基于LSTM改进的RNN模型,将网络爬取的数据输入到模型进行训练和测试。结果表明,语义规则抽取出的特征包含更多、更准确的情感信息,使得混合特征模板可以更加全面地考虑文本的情感特征粒度;Fisher准则可有效识别出高判别性的低维文本特征,进一步提高改进RNN模型对评论文本的分类性能。     

面向产品设计改进的在线评论挖掘

以竞争市场环境中的产品在线评论数据为研究对象,基于支持产品设计改进的视角,采用数据挖掘的方法与工具,开展面向产品设计改进的在线评论大数据分析研究。重点开展在线评论数据挖掘过程模型中的有用性建模和特征评价值情感分析。以某智能手机产品的在线评论数据为对象进行了实验,得到该产品各个属性的评价值,与更新换代后的产品属性进行比较,验证了此方法的有效性。     

面向中文网络评论情感分类的集成学习框架

该文针对中文网络评论情感分类任务,提出了一种集成学习框架。首先针对中文网络评论复杂多样的特点,采用词性组合模式、频繁词序列模式和保序子矩阵模式作为输入特征。然后采用基于信息增益的随机子空间算法解决文本特征繁多的问题,同时提高基分类器的分类性能。最后基于产品属性构造基分类器算法综合评论文本中每个属性的情感信息,进而判别评论的句子级情感倾向。实验结果表明了该框架在中文网络评论情感分类任务上的有效性,特别是在Logistic Regression分类算法上准确率达到90.3%。