Multi-opinion Ring: visualizing and predicting multiple opinion orientations in online social media

In the era of the Social Web, actors (e.g. people, organizations, nations, etc) of online social media often voice out their opinions towards a variety of opinion targets. Extracting and visualizing distributions of multiple opinions among actors facilitates individuals or organizations to extract valuable social intelligence from online social media. The main contribution of our research reported in this paper is the development of a novel opinion analysis methodology named Multi-opinion Ring for visualizing and predicting multiple opinion orientations held by different groups of actors in online social media. In particular, the proposed Multi-opinion Ring method combines visualization techniques with machine learning methods to predict the opinion inclinations of actors who are originally neutral to different opinion targets. A series of controlled experiments, user-based evaluations, and case studies show that the proposed Multi-opinion Ring method significantly outperforms classical visualization methods in terms of the cohesiveness of the graphical layout and the informativeness of the visualized contents.     

Text Indexation with INTEX

INTEX is a linguistic development environment that includes large-coverage dictionaries and grammars, and parses texts of several million words in real time. INTEX has tools to create and maintain large-coverage lexical resources as well as morphological and syntactic grammars. Dictionaries and grammars are applied to texts in order to locate morphological, lexical and syntactic patterns, remove ambiguities, and tag simple and compound words. INTEX can build lemmatized concordances and indices of large texts with respect to all types of Finite State patterns. INTEX is used as a corpus processor, to analyze literary, journalistic and technical texts. I describe here the subset of tools used to perform advanced search requests on large texts. This revised version was published online in July 2006 with corrections to the Cover Date.     

Text Indexation with INTEX

INTEX is a linguistic development environment that includes large-coverage dictionaries and grammars, and parses texts of several million words in real time. INTEX has tools to create and maintain large-coverage lexical resources as well as morphological and syntactic grammars. Dictionaries and grammars are applied to texts in order to locate morphological, lexical and syntactic patterns, remove ambiguities, and tag simple and compound words. INTEX can build lemmatized concordances and indices of large texts with respect to all types of Finite State patterns. INTEX is used as a corpus processor, to analyze literary, journalistic and technical texts. I describe here the subset of tools used to perform advanced search requests on large texts. This revised version was published online in July 2006 with corrections to the Cover Date.     

Warning system for online market research – Identifying critical situations in online opinion formation

More and more consumers are relying on online opinions when making purchasing decisions. For this reason, companies must have knowledge of the actual standing of their products on the Web. A warning system for online market research is being proposed which allows the identification of critical situations in online opinion formation. When critical situations are detected, warnings are subsequently sent to marketing managers and thus allowing marketers the ability to initiate preventive measures. The warning system operates on a knowledge base which contains product-related success values, online opinions and patterns of social interactions. This knowledge is acquired using methods coming from information extraction, text mining and social network analysis. Based on this knowledge the warning system judges situations accordingly. For this purpose, a neuro-fuzzy approach is chosen which learns linguistic rules from data. These rules are employed to estimate future situations. The warning system is applied to two scenarios and yields good results. An evaluation shows that all components of the warning system outperform alternative methods.     

A Consensus Support System Model for Group Decision-Making Problems With Multigranular Linguistic Preference Relations

The group decision-making framework with linguistic preference relations is studied. In this context, we assume that there exist several experts who may have different background and knowledge to solve a particular problem and, therefore, different linguistic term sets (multigranular linguistic information) could be used to express their opinions. The aim of this paper is to present a model of consensus support system to assist the experts in all phases of the consensus reaching process of group decision-making problems with multigranular linguistic preference relations. This consensus support system model is based on i) a multigranular linguistic methodology, ii) two consensus criteria, consensus degrees and proximity measures, and iii) a guidance advice system. The multigranular linguistic methodology permits the unification of the different linguistic domains to facilitate the calculus of consensus degrees and proximity measures on the basis of experts' opinions. The consensus degrees assess the agreement amongst all the experts' opinions, while the proximity measures are used to find out how far the individual opinions are from the group opinion. The guidance advice system integrated in the consensus support system model acts as a feedback mechanism, and it is based on a set of advice rules to help the experts change their opinions and to find out which direction that change should follow in order to obtain the highest degree of consensus possible. There are two main advantages provided by this model of consensus support system. Firstly, its ability to cope with group decision-making problems with multigranular linguistic preference relations, and, secondly, the figure of the moderator, traditionally presents in the consensus reaching process, is replaced by the guidance advice system, and in such a way, the whole group decision-making process is automated     

中文网络评论的产品特征提取及情感倾向判定

随着互联网和电子商务的发展,用户在购买或使用商品之后会在网络站点上发表对产品的评论,大量的产品评论中所包含的丰富信息,可以为生产厂商和用户提供重要的决策依据。基于文本的语义和语言分析,提出了从产品评论中提取用户关注的产品特征的方法,并根据用户的关注程度对产品特征进行排序;同时,根据观点词的极性值判定用户对产品特征的情感倾向以及情感倾向强度。本研究采用从互联网上获得的针对笔记本电脑的产品评论作为实验对象,实验结果初步证明该方法具有良好的准确率和召回率。     

User opinion classification in social media: A global consistency maximization approach

Social media is a major platform for opinion sharing. In order to better understand and exploit opinions on social media, we aim to classify users with opposite opinions on a topic for decision support. Rather than mining text content, we introduce a link-based classification model, named global consistency maximization (GCM) that partitions a social network into two classes of users with opposite opinions. Experiments on a Twitter data set show that: (1) our global approach achieves higher accuracy than two baseline approaches and (2) link-based classifiers are more robust to small training samples if selected properly.     

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。     

Ranking entities on the basis of users’ opinions

Online opinions are one of the most important sources of information on which users base their purchasing decisions. Unfortunately, the large quantity of opinions makes it difficult for an individual to consume in a reasonable amount of time. Unlike standard information retrieval problems, the task here is to retrieve entities whose relevance is dependent upon other people’s opinions regarding the entities and how well those sentiments match the user’s own preferences. We propose novel techniques that incorporate aspect subjectivity measures into weighting the relevance of opinions of entities based on a user’s query keywords. We calculate these weights using sentiment polarity of terms found proximity close to keywords in opinion text. We have implemented our techniques, and we show that these improve the overall effectiveness of the baseline retrieval task. Our results indicate that on entities with long opinions our techniques can perform as good as state-of-the-art query expansion approaches.     

Unsupervised method for sentiment analysis in online texts

In recent years, the explosive growth of online media, such as blogs and social networking sites, has enabled individuals and organizations to write about their personal experiences and express opinions. Classifying these documents using a polarity metric is an arduous task. We propose a novel approach to predicting sentiment in online textual messages such as tweets and reviews, based on an unsupervised dependency parsing-based text classification method that leverages a variety of natural language processing techniques and sentiment features primarily derived from sentiment lexicons. These lexicons were created by means of a semiautomatic polarity expansion algorithm in order to improve accuracy in specific application domains. The results obtained for the Cornell Movie Review, Obama-McCain Debate and SemEval-2015 datasets confirm the competitive performance and the robustness of the system.     

Semi-automatic construction of a named entity dictionary for entity-based sentiment analysis in social media

To understand the user experience in social media or to facilitate the design of human-centric services by social media, users’ opinions about specific entities in text messages should be captured. A fine-grained named entity recognizer (NER) is an essential module for identifying opinion targets in text messages, and a named-entity (NE) dictionary is a major resource that affects the performance of an NER. However, it is not easy to construct an NE dictionary manually, because human annotation is time-consuming and labor-intensive. To reduce construction time and labor, we propose a semi-automatic system to construct an NE dictionary from the free online resource, Wikipedia. The proposed system constructs a pseudo-document for each Wikipedia NE by using an active-learning technique. It then classifies Wikipedia entries into NE classes based on similarities between the entries and pseudo-documents located in a vector space. In experiments, the proposed system classified 92.3 % of Wikipedia entries into 29 NE classes. It showed a high performance, with a macro-averaging F1-measure of 0.872 and micro-averaging F1-measure of 0.935.     

A general class of simple majority decision rules based on linguistic opinions

In this paper we have introduced a class of decision rules related to simple majority, by considering individual intensities of preference. These intensities will be shown by means of linguistic labels. In order to compare the amount of opinion obtained by each alternative, we have considered the total ordered monoid generated by the sums of the original labels, according to an addition and an ordering. In this general framework different sets of linguistic labels can be employed and these sets can be represented by means of diverse mathematical objects. Moreover, on these mathematical representations of linguistic labels several orderings can be considered. Thus, flexibility is an important feature of this new class of group decision making procedures. Some examples of putting in practice the simple majority decision rules based on linguistic labels are provided, and the main properties of these voting systems are analyzed. It is worth emphasizing that these properties are satisfied for any total ordered monoid, regardless of the mathematical representation of linguistic labels or the ordering used to compare collective opinions.     

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。     

Social Media Analysis and Public Opinion: The 2010 UK General Election

Social media monitoring in politics can be understood by situating it in theories of public opinion. The multimethod study we present here indicates how social media monitoring allow for analysis of social dynamics through which opinions form and shift. Analysis of media coverage from the 2010 UK General Election demonstrates that social media are now being equated with public opinion by political journalists. We use interviews with pollsters, social media researchers and journalists to examine the perceived link between social media and public opinion. In light of competing understandings these interviews reveal, we argue for a broadening of the definition of public opinion to include its social dimension.     

Combination of Qualitative Information with 2-Tuple Linguistic Representation in DSmT

Modern systems for information retrieval,fusion and management need to deal more and more with information coming from human experts usually expressed qualitatively in natural language with linguistic labels.In this paper,we propose and use two new 2-Tuple linguistic representation models(i.e.,a distribution function model(DFM) and an improved Herrera-Martínez's model) jointly with the fusion rules developed in Dezert-Smarandache Theory(DSmT),in order to combine efficiently qualitative information expres...     

Journalists as Crowdsourcerers: Responding to Crisis by Reporting with a Crowd

Widespread adoption of new information communication technologies (ICTs) is disrupting traditional models of news production and distribution. In this rapidly changing media landscape, the role of the journalist is evolving. Our research examines how professional journalists within a rural community impacted by Hurricane Irene successfully negotiated a new role for themselves, transforming their journalistic practice to serve in a new capacity as leaders of an online volunteer community. We describe an emergent organization of media professionals, citizen journalists, online volunteers, and collaborating journalistic institutions that provided real-time event coverage. In this rural context, where communications infrastructure is relatively uneven, this ad hoc effort bridged gaps in ICT infrastructure to unite its audience. In this paper, we introduce a new perspective for characterizing these information-sharing activities: the “human powered mesh network” extends the concept of a mesh network to include human actors in the movement of information. Our analysis shows how journalists played a key role in this network, and facilitated the movement of information to those who needed it. These findings also note a contrast between how HCI researchers are designing crowdsourcing platforms for news production and how crowdsourcing efforts are forming during disaster events, suggesting an alternative approach to designing for emergent collaborations in this context.     

Opinion mining from noisy text data

The proliferation of Internet has not only led to the generation of huge volumes of unstructured information in the form of web documents, but a large amount of text is also generated in the form of emails, blogs, and feedbacks, etc. The data generated from online communication acts as potential gold mines for discovering knowledge, particularly for market researchers. Text analytics has matured and is being successfully employed to mine important information from unstructured text documents. The chief bottleneck for designing text mining systems for handling blogs arise from the fact that online communication text data are often noisy. These texts are informally written. They suffer from spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on opinion extraction from noisy text data. It is aimed at extracting and consolidating opinions of customers from blogs and feedbacks, at multiple levels of granularity. We have proposed a framework in which these texts are first cleaned using domain knowledge and then subjected to mining. Ours is a semi-automated approach, in which the system aids in the process of knowledge assimilation for knowledge-base building and also performs the analytics. Domain experts ratify the knowledge base and also provide training samples for the system to automatically gather more instances for ratification. The system identifies opinion expressions as phrases containing opinion words, opinionated features and also opinion modifiers. These expressions are categorized as positive or negative with membership values varying from zero to one. Opinion expressions are identified and categorized using localized linguistic techniques. Opinions can be aggregated at any desired level of specificity i.e. feature level or product level, user level or site level, etc. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions crawled from a set of pre-defined blogs.     

基于领域识别的主题模型观点挖掘研究

网络新媒体的快速发展,使得网上评论数据呈现爆炸性增长,面对数量庞大的网络文本,使用传统的人工方式来提取观点会导致效率低下、分类界限模糊、领域适应性差等问题。为解决以上问题,在对传统LDA模型进行改进的基础上,提出了一个基于领域判别的LDA主题模型来对在线评论进行观点挖掘。首先,在标准LDA模型中引入领域层,对语料库中的文档采样领域标签,利用领域化的参数来求解LDA模型;其次,考虑到句子间的情感从属关系,在主题层和单词层之间加入情感层,并引入情感转移变量进行表示,提高了情感极性分析的精度,实验结果表明了本文所提模型和理论的有效性。     

互联网舆情监管与应对技术探究

当前,随着互联网的技术性、自由性、互动性、普及性的发展,互联网正成为社会各种利益诉求的表达、意识形态较量的媒介,成为社会公众关注时事政治、评论时事政治的公共平台,成为快速表达民意的渠道。这就要求有关部门要对网络信息内容实施有效监管,以保证社会的安全与稳定,而要实现对海量的网上内容的有效监管,就必须得到网络舆情监管技术的支撑。文章介绍和分析了中国网络舆情发展的现状和网络内容监管技术的发展方向,对网络舆情采集与获取技术、舆情处理技术进行了集中分析。