酒店在线评论数据的特征挖掘

论文以酒店在线评论数据为研究对象，对酒店在线评论数据的特征挖掘进行了研究。论文首先从酒店在线评论数据的获取出发，经过数据清洗、词性分析、特征抽取、指标确定、特征筛选、特征确定、特征校验几个环节，实现了酒店在线评论数据特征挖掘的目的。论文以词频为基础，融合了词性分析、聚类分析等方法，利用词频数（TF）、词频率（TF₁）、词频权重（TTW）、评论频率（DF）、逆文档频率（IDF）和TF1-IDF等指标对候选特征词进行降维，得出酒店在线评论数据的特征，并对特征词进行校验，完成了酒店在线评论数据的特征挖掘的过程。论文将为以评论为依据的客户分类、酒店分类、智能推荐奠定基础。     

中文产品评论结构化引擎

为实现网络上非领域相关的中文产品评论信息的搜索及结构化,设计并实现中文产品评论结构化引擎,该引擎通过抓取网络上指定产品的评论,抽取评论中的特征与意见,判断意见的极型,将评论以（特征,意见,极型）的结构化形式输出给用户。其中特征意见抽取解决了产品隐形特征抽取问题。实验结果表明该引擎的特征意见召回率与准确率、意见极型判断准确率等指标可以满足用户需要。     

基于全局用户意图的评论自动估价方法研究

评论是一种反映事物价值的重要主观信息。该文从用户角度出发,提出一种基于全局用户意图的商品评论自动估价方法。该研究首先定义了一种简易的评论价值划分标准(“实用”和“垃圾”评论),借以实现前瞻性的方法尝试。在此基础上,该文采用SVM分类器作为划分评论价值类别(二元分类问题)的基本平台,并基于这一平台重点考察三种影响评论价值的特征 1)属性热度;2)内容可信度;3)用户情感和观点。该文在文本结构特征的基础上,加入上述三类反映用户意图的特征进行评论价值判定,并在大规模商品评论语料集中进行测试。实验表明通过引入用户意图特征,评论自动估价的性能有较大幅度提高。     

基于情感词典的酒店评论情感分析与可视化

为研究高端酒店服务中的亮点和不足,分析酒店用户评论舆情,文章对高端酒店用户评论进行情感分析和可视化,提出酒店优势与改进策略。文章采用通用情感词典Hownet与酒店评论相关的评论领域专业词典相结合的方式构建领域情感词典。结合所构建的领域情感词典和其他特殊词典,比如短语词典、否定词词典和副词词典等进行情感分类,然后将分类完成的三个极性的情感词进行词频统计和词云绘制,最后根据词云结果,给出高端酒店在经营策略上的改进建议。     

Multiaspect‐based opinion classification model for tourist reviews

Tourist reviews on social media websites reflect the tourist's opinions concerning various aspects of a tourist place or service (e.g., “comfortable room” and “terrible service” in hotel reviews). Extracting these aspects from reviews is a challenging task in opinion mining. Therefore, aspect‐based opinion mining has emerged as a new area of social review mining. Existing approaches in this area focus on extracting explicit aspects and classification of opinions around these aspects. However, the implicit and coreferential aspects during aspect extraction are often neglected, and the classification of multiaspect opinions is relatively less emphasized in prior art. In this paper, we propose a model, namely, “enhanced multiaspect‐based opinion classification” that addresses existing challenges by automatically extracting both explicit and implicit aspects and classifying the multiaspect opinions. In this model, first, a probabilistic co‐occurrence‐based method is proposed that utilizes the co‐occurrence between aspects and sentiment words to identify the coreferential aspects and merge them into groups. Second, an implicit aspect extraction method is proposed that associates the sentiment words with suitable aspects to build an aspect‐sentiment hierarchy. Third, a multiaspect opinion classification approach is proposed that employs multilabel classification algorithms to classify opinions into different polarity classes. The effectiveness of the proposed model is evaluated by conducting experiments on benchmark and real‐world datasets. The experimental results revealed the supremacy of multilabel classifiers by achieving 90% accuracy per label on classification when extracting 87% domain‐relevant aspects. A state‐of‐the‐art performance comparison is conducted that also verifies the advantages of the proposed model.     

中文网络评论中提取产品特征的研究

大量的网络评论已经成为挖掘用户意见、改进产品质量的重要信息来源,而特征抽取作为后续分析的基础,直接影响到最终意见挖掘结果的准确性. 本文提出了一种PMI-Bootstrapping算法,并结合了语言规则实现中文网络评论的产品特征抽取. 首先利用语言规则产生候选特征集,计算每个候选特征与初始给定种子集的加权平均互信息,将满足阈值的候选特征添加到种子集中,如此循环迭代,直到种子集合收敛,输出排队后的种子集合作为抽取结果. 实验证明,该算法取得良好的准确率和召回率.     

中文网络评论的IT产品特征挖掘及情感倾向分析

为探索中文客户评论中的IT产品特征及相关情感倾向的挖掘,帮助IT生产商和服务商提高改进产品和服务质量,提高竞争力。该文将采用情感分析技术,提出基于客户感知价值的产品特征挖掘算法,实现对于评论中IT产品特征及其情感倾向的语义分析、动态提取和综合信息挖掘;并根据用户的关注权重将产品特征和情感倾向进行排列。采用从互联网下载的真实IT产品评论语料中进行实验,初步验证了该方法的有效性。     

Implicit feature identification via hybrid association rule mining

In sentiment analysis, a finer-grained opinion mining method not only focuses on the view of the product itself, but also focuses on product features, which can be a component or attribute of the product. Previous related research mainly relied on explicit features but ignored implicit features. However, the implicit features, which are implied by some words or phrases, are so significant that they can express the users’ opinion and help us to better understand the users’ comments. It is a big challenge to detect these implicit features in Chinese product reviews, due to the complexity of Chinese. This paper is mainly centered on implicit features identification in Chinese product reviews. A novel hybrid association rule mining method is proposed for this task. The core idea of this approach is mining as many association rules as possible via several complementary algorithms. Firstly, we extract candidate feature indicators based word segmentation, part-of-speech (POS) tagging and feature clustering, then compute the co-occurrence degree between the candidate feature indicators and the feature words using five collocation extraction algorithms. Each indicator and the corresponding feature word constitute a rule (feature indicator → feature word). The best rules in five different rule sets are chosen as the basic rules. Next, three methods are proposed to mine some possible reasonable rules from the lower co-occurrence feature indicators and non indicator words. Finally, the latest rules are used to identify implicit features and the results are compared with the previous. Experiment results demonstrate that our proposed approach is competent at the task, especially via using several expanding methods. The recall is effectively improved, suggesting that the shortcomings of the basic rules have been overcome to certain extent. Besides those high co-occurrence degree indicators, the final rules also contain uncommon rules.     

Sentiment analysis leveraging emotions and word embeddings

Sentiment analysis and opinion mining are valuable for extraction of useful subjective information out of text documents. These tasks have become of great importance, especially for business and marketing professionals, since online posted products and services reviews impact markets and consumers shifts. This work is motivated by the fact that automating retrieval and detection of sentiments expressed for certain products and services embeds complex processes and pose research challenges, due to the textual phenomena and the language specific expression variations. This paper proposes a fast, flexible, generic methodology for sentiment detection out of textual snippets which express people’s opinions in different languages. The proposed methodology adopts a machine learning approach with which textual documents are represented by vectors and are used for training a polarity classification model. Several documents’ vector representation approaches have been studied, including lexicon-based, word embedding-based and hybrid vectorizations. The competence of these feature representations for the sentiment classification task is assessed through experiments on four datasets containing online user reviews in both Greek and English languages, in order to represent high and weak inflection language groups. The proposed methodology requires minimal computational resources, thus, it might have impact in real world scenarios where limited resources is the case.     

基于汉语组块的产品特征观点对提取与情感分析研究

为了解决用户评论文本中的产品特征-观点对的提取及情感分析问题,本文利用组块分析提取产品特征,从中寻找到频繁项集,并用PMI对候选产品特征进行过滤,得到产品的特征集合;利用特征与情感词在位置上的邻接关系,提取情感词并组成特征-观点对,通过SO-PMI方法进行情感倾向分析。为验证该方法的有效性,以酒店评论文本为例,从中提取酒店的特征-观点对并进行情感分析,准确率为76.68%,召回率为70.84%。实验结果表明引入组块分析可以有效的解决商品评论的细粒度情感分类问题。     

基于CRF的中文评论有效性挖掘产品特征

方面级意见挖掘的任务通常包括从客户评论中抽取产品的特征、与产品特征相关联的观点词识别以及观点的极性判断三个方面。围绕如何实现中文评论的方面级意见挖掘问题,提出了利用条件随机场实现中文评论的方面级意见挖掘的四个主要步骤：数据预处理、训练集准备、为条件随机场模型定义学习函数、应用模型标注新的评论数据。在此基础上,通过以五种实际产品的中文评论语料为数据集,对该方法进行了数据实验。实验结果表明,该方法针对不同类型观点元素的抽取在评估性能指标上大部分达到或超过80%。为了进一步验证所提出方法的有效性,将研究结果进行了差异显著性检验。结果显示,用CRF对中文评论进行方面级意见挖掘和对英文评论的方面意见挖掘的性能差异不大。最后,比较了三种不同方法的方面抽取精度和情感分类精度,实验结果表明,CRF方法优于词典化的隐马尔可夫模型和关联规则挖掘方法。     

基于情感从属和最大熵模型的细粒度观点挖掘

目前许多观点挖掘方法挖掘粒度过大,导致反馈信息不足。为解决该问题,对标准LDA模型进行改进,提出主题情感联合最大熵LDA模型进行细粒度观点挖掘。首先,考虑到词的位置和语义信息,在传统LDA模型中加入最大熵组件来区分背景词、特征词和观点词,并对特征词和观点词进行局部和全局的划分;其次,在主题层和单词层之间加入情感层,实现词语级别的细粒度情感分析,并引入情感转移变量来处理情感从属关系,同时获取整篇评论和每个主题的情感极性,实验验证了所提模型和理论的有效性。     

基于SVM的酒店客户评论情感分析

通过增加情感词典种类提高系统对网络词汇、表情符号进行分词和情感分析的准确性;以某酒店的客户评论为原始数据,提取正负向情感词的数量、否定词、程度副词以及特殊符号数量等文本特征后进行不同的特征组合,通过K重交叉验证和网格搜索算法找到SVM（支持向量机）算法的最优参数组合C和g。采用SVM对不同的特征组合进行训练测试并对每个组合的正确率进行分析,然后找出最适合用户评论情感分析的文本特征及特征组合。结果表明：在每个特征组合获取其最优的C和g参数组合的前提下,选用正负向情感词、否定词、情感分值、程度副词的特征组合测试正确率最高,达到93.4%。     

基于观点袋模型的汽车评论情感极性分类

该文针对网络评论倾向分级问题,提出了一种基于观点袋模型和语言学规则的多级情感分类方法。通过分析句子中的词性搭配关系,设计了12种抽取特征-观点搭配模式,并对存在问题给出了解决策略。依据汉语用词特点和词汇在汽车领域的特殊用法,提出搭配四元组的情感倾向极性值计算方法。在此基础上,利用获取的搭配四元组及其情感倾向极性,建立文本的向量化表示,并构造了权重计算公式。最后,利用文本余弦相似度计算方法实现对评论文本的五级情感极性分类。通过在COAE2012任务3的汽车数据集上进行的测试,取得了较好的分类结果。     

Combining user preferences and user opinions for accurate recommendation

Recommendation systems represent a popular research area with a variety of applications. Such systems provide personalized services to the user and help address the problem of information overload. Traditional recommendation methods such as collaborative filtering suffer from low accuracy because of data sparseness though. We propose a novel recommendation algorithm based on analysis of an online review. The algorithm incorporates two new methods for opinion mining and recommendation. As opposed to traditional methods, which are usually based on the similarity of ratings to infer user preferences, the proposed recommendation method analyzes the difference between the ratings and opinions of the user to identify the user’s preferences. This method considers explicit ratings and implicit opinions, an action that can address the problem of data sparseness. We propose a new feature and opinion extraction method based on the characteristics of online reviews to extract effectively the opinion of the user from a customer review written in Chinese. Based on these methods, we also conduct an empirical study of online restaurant customer reviews to create a restaurant recommendation system and demonstrate the effectiveness of the proposed methods.     

轻型评论的情感分析研究

以在智能移动设备上发表的用户评论作为研究对象,并将该类评论称为轻型评论。指出了轻型评论与早期互联网评论及短文本研究的异同点,并通过实验总结轻型评论的独有特性：字数少、跨度大,短小评论数量众多,评论长度与数量满足幂率分布。同时,针对轻型评论的情感分类研究展开了一系列的实验研究,发现：(1)情感分类效果随着评论长度的增加而下降;(2)传统的特征筛选方法以及特征加权方法对于轻型评论效果都不够理想;(3)极性词在短评论中比例高于长评论;(4)长、短评论在用词上存在较高的重叠度。在此基础上,提出了一种基于短评论特征共现的特征筛选方法,将短小评论中的优势信息和传统的特征筛选方法相结合,在筛选掉无用噪音的同时增补有利于分类的有效特征。实验结果表明,该方法可以有效地提高轻型评论中较长评论的分类效果。     

Textual factors in online product reviews: a foundation for a more influential approach to opinion mining

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.     

SumView: A Web-based engine for summarizing product reviews and customer opinions

In this paper, we develop SumView, a Web-based review summarization system, to automatically extract the most representative expressions and customer opinions in the reviews on various product features. Different from existing review analysis which makes more efforts on sentiment classification and opinion mining, our system mainly focuses on summarization, i.e., delivering the majority of information contained in the review documents by selecting the most representative review sentences for each extracted product feature. Comprehensive case studies and experiments demonstrate the effectiveness of our system, and the user study shows users’ satisfaction.     

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。