基于多线索混合词典的微博情绪识别

微博等社交媒体为人们情绪表达提供了重要平台,分析微博的情绪倾向具有重要的商业价值和社会意义。文中提出了基于词典的规则方法识别微博所表达的喜、哀、怒、惧、恶、惊六种情绪。针对情绪表达的重要线索表情符利用互信息法生成了表情符词典,与传统情绪词典相结合,制定了针对否定用法的规则对微博进行分析。建立了第一个包含六种情绪的人工标注微博数据集。实验表明,传统的情绪词典虽然收录了大量词汇,但对于社交媒体文本分析的准确率和覆盖率都不高。表情符词典的应用显著地提高了微博情绪分析的精度和覆盖率。     

基于多情绪源关联模型的中文微博情感分析

社交媒体信息的爆炸式增长,使得依据其对公众舆论情感的分析受到越来越多的关注。与传统文本不同,新浪微博中存在包括情感词、表情、图片和视频等特征在内的多情绪源,本文针对中文社交短文本情感分析中情感词典时效性问题和多情绪源间的关联性问题,提出了一种多情绪源关联模型。该模型考虑微博中的情感词和表情特征及其之间的关联关系,在经典的词典规则投票方法基础上,引入多情绪源以及关联概率,通过概率建模的方式对情感词和表情两类情绪源建立关联模型,实现对微博情感的判别。实验表明,在6 171条微博数据集中,多情绪源关联模型分类准确率达到了85.3%,强于包含情感词和表情的传统投票模型(83.4%)以及包含同类多特征的SVM方法(82.9%)。     

基于词典的中文微博情绪识别

微博等社交媒体已成为表达个人情绪和感受的重要平台。自动分析微博文本表达的情绪对于迅速了解大众情绪走向以及调节个人情绪有着重要的意义。文中首次针对中文微博中的情绪进行自动分析,识别微博表达的喜、哀、怒、惧情绪。提出以词典为依据的基于规则的方法,通过实验详细分析了中文情绪词典在社交媒体文本分析中的现状,讨论了存在的主要问题。并深入讨论了微博中情绪表达的语言特点,为建立高精度的情绪分析系统提供了依据。     

面向微博文本的情绪标注语料库构建

文本情绪分析研究近年来发展迅速,但相关的中文情绪语料库,特别是面向微博文本的语料库构建尚不完善。为了对微博文本情绪表达特点进行分析以及对情绪分析算法性能进行评估,该文在对微博文本情绪表达特点进行深入观察和分析的基础上,设计了一套完整的情绪标注规范。遵循这一规范,首先对微博文本进行了微博级情绪标注,对微博是否包含情绪及有情绪微博所包含的情绪类别进行多标签标注。而后,对微博中的句子进行有无情绪及情绪类别进行标注,并标注了各情绪类别对应的强度。目前,已完成14000条微博,45431句子的情绪标注语料库构建。应用该语料库组织了NLP&CC2013中文微博情绪分析评测,有力地促进了微博情绪分析相关研究。     

基于神经网络的微博情绪识别与诱因抽取联合模型

情绪诱因抽取作为深层次的文本情绪理解已成为情绪分析任务中的新热点,当前研究通常把诱因抽取和情绪识别看作两个独立的任务,容易导致错误在任务间的传播问题。考虑到情绪识别及诱因抽取是相互作用的,以及微博文本中表情符通常表达文本的情绪,提出了一种基于双向长短期记忆条件随机场（Bi-LSTM-CRF）模型的情绪诱因和表情符情绪识别的联合模型。该模型将情绪诱因抽取以及情绪识别形式化为一个统一的序列标注问题,充分利用了情绪诱因与情绪之间的互相作用,将情绪诱因的抽取和情绪识别同时进行。实验结果表明,该模型在诱因抽取任务中的F值为82.70%,在情绪识别任务中的F值为74.74%,相比串行模型的F值分别提高5.82和17.12个百分点,这个结果表明联合模型能够有效降低任务串行进行时的误差传递,同时提高了诱因抽取和情绪识别的F值。     

基于种子词的微博表情符情感倾向判定方法

情感倾向明显的表情符,容易通过人工进行标注。但是对于情感倾向不明显的表情符,多人手工的标注结果往往难以达成一致。因此,提出一种利用种子词自动判定表情符情感倾向的方法。该方法利用少量种子表情符自动标注情感倾向比较明显的表情符,生成表情符标注 集;对于情感倾向不明显的表情符,利用种子情感词和已得到的表情符标注集构建模型,实现其情感倾向的自动判定。实验结果表明,本文方法在微博表情符情感倾向的自动判 定上有很好的效果。     

细粒度微博情绪识别的集成算法研究

目前大部分微博情绪分析研究集中在粗粒度情绪的划分,但细粒度微博情绪更能反映公众对舆论热点、政策的反应.因此提出了一种结合朴素贝叶斯和K最近邻的集成算法,着重对新浪微博展开了情绪识别与分析的研究.首先采用朴素贝叶斯分类算法将微博分为有无情绪两类.然后根据情绪本体库的分类规则,分别构建待预测微博和已标注微博的21维情绪向量.最后采用K最近邻算法,计算待预测情绪微博与已标注情绪微博的向量相似度,从而获取待预测微博的细粒度情绪.实验表明K最近邻算法的引入,在微博细粒度情绪识别的准确率上取得了较好的效果.     

一种基于情绪激励度的情绪词加权方法

在不同的上下文中,情绪词对情绪的激励程度会发生变化。现有情绪词典中大多数只标注了情绪词的情绪类别而未涉及情绪词的激励度。在极少数标注情绪强度的词典中,所标注的强度未考虑上下文的影响。提出一种根据上下文形成的情境评估情绪词对情绪的激励程度并据此对情绪词加权的方法。通过比较情绪词的共现模式与自身情绪类的分布模式计算情绪词的激励程度。然后根据激励程度计算情绪词的情绪权重并将其用于微博情绪识别。实验结果表明,与现有词典中的情绪强度相比,本文方法计算的情绪权重更准确地描述了情绪词在语料中表达的情绪,有效地提高了情绪分析的精度。并且本文方法还能够有效综合多个词典的优势,进一步提高微博情绪分析的准确率。     

一种消除情绪孤立点的中文微博情绪分析

随着互联网的迅速发展,微博已经成了人们抒发个人情绪的重要平台,对微博文本的意见挖掘和情绪分析也受到了大量学者的关注和研究.其中,由于微博主题的发散性以及情绪的多元性,导致微博文本中出现了大量的情绪孤立点,干扰了微博情绪的判断.因此,论文提出通过情绪相似度的方法来消除语料中的情绪孤立点,并利用规则方法来判断微博文本的情绪.实验表明,消除语料中的情绪孤立点有效地提高微博情绪分析的准确性和精确率.     

基于表情符注意力机制的微博情感分析模型

为了对中文微博进行有效的情感极性识别,基于表情符能改变或加强微博文本的情感极性这一认知事实,提出基于表情符注意力机制的微博情感分析神经网络模型。该模型在使用双向循环神经网络模型(BiLSTM)学习文本的特征表示时,利用表情符注意力机制,得到文本结合表情符后新的特征表示,从而实现微博情感识别。实验结果显示,与输入纯文本和表情符的Bi-LSTM模型相比,基于表情符注意力机制的模型准确率提高了4. 06%;与仅输入纯文本的Bi-LSTM模型相比,基于表情符注意力机制的模型准确率提高了6. 35%。     

Automatically annotating a five-billion-word corpus of Japanese blogs for sentiment and affect analysis

This paper presents our research on automatic annotation of a five-billion-word corpus of Japanese blogs with information on affect and sentiment. We first perform a study in emotion blog corpora to discover that there has been no large scale emotion corpus available for the Japanese language. We choose the largest blog corpus for the language and annotate it with the use of two systems for affect analysis: ML-Ask for word- and sentence-level affect analysis and CAO for detailed analysis of emoticons. The annotated information includes affective features like sentence subjectivity (emotive/non-emotive) or emotion classes (joy, sadness, etc.), useful in affect analysis. The annotations are also generalized on a two-dimensional model of affect to obtain information on sentence valence (positive/negative), useful in sentiment analysis. The annotations are evaluated in several ways. Firstly, on a test set of a thousand sentences extracted randomly and evaluated by over forty respondents. Secondly, the statistics of annotations are compared to other existing emotion blog corpora. Finally, the corpus is applied in several tasks, such as generation of emotion object ontology or retrieval of emotional and moral consequences of actions.     

The effect of emoticons in simplex and complex task-oriented communication: An empirical study of instant messaging

Many studies have shed light on general computer-mediated communication, instant messaging (IM), and emotion or emoticons, but little is known specifically about the impacts of emoticons in task-oriented IM communication in the workplace. Therefore, the current study addresses this issue by conducting an exploratory experiment to (1) categorize workplace IM messages into coherent groups, (2) identify the most commonly used emoticons (emblems) for expressing positive, negative, and neutral emotions in the case company, (3) test the differences in the emotional effects of the received text messages with and without emoticons on the reader/s, and (4) examine the intention to use emoticons in IM in the workplace. The results showed that (1) negative emoticons could cause a negative effect in both simplex and complex task-oriented communication, (2) positive emoticons only created a positive effect in complex communication and for female employees in simplex communication, and (3) there is no significant difference between task-oriented messages with or without neutral emoticon. Furthermore, the intention of using emoticons was not statistically significant in terms of gender, but it has a higher tendency on female employees. The corresponding suggestions provided by this research may help increase our understanding on the effect of emoticon use in IM in the workplace.     

Using Hashtags to Capture Fine Emotion Categories from Tweets

Detecting emotions in microblogs and social media posts has applications for industry, health, and security. Statistical, supervised automatic methods for emotion detection rely on text that is labeled for emotions, but such data are rare and available for only a handful of basic emotions. In this article, we show that emotion‐word hashtags are good manual labels of emotions in tweets. We also propose a method to generate a large lexicon of word–emotion associations from this emotion‐labeled tweet corpus. This is the first lexicon with real‐valued word–emotion association scores. We begin with experiments for six basic emotions and show that the hashtag annotations are consistent and match with the annotations of trained judges. We also show how the extracted tweet corpus and word–emotion associations can be used to improve emotion classification accuracy in a different nontweet domain. Eminent psychologist Robert Plutchik had proposed that emotions have a relationship with personality traits. However, empirical experiments to establish this relationship have been stymied by the lack of comprehensive emotion resources. Because personality may be associated with any of the hundreds of emotions and because our hashtag approach scales easily to a large number of emotions, we extend our corpus by collecting tweets with hashtags pertaining to 585 fine emotions. Then, for the first time, we present experiments to show that fine emotion categories such as those of excitement, guilt, yearning, and admiration are useful in automatically detecting personality from text. Stream‐of‐consciousness essays and collections of Facebook posts marked with personality traits of the author are used as test sets.     

How affective technologies can influence intimate interactions and improve social connectedness

Affective computing holds the promise of creating effortless, integrated, and automatic ways of communicating emotions within our intimate social network. This could augment awareness systems and connectedness devices, reducing loneliness and improving health and well-being. Through two experiments, we investigate the effects of quantity and automaticity of emotion communication on perceived intimacy in mediated settings. In the first experiment (N=48), we manipulated the number of communicated emoticons. Results show that increases in communicated emoticon quantity lead to strong increases in perceived intimacy. In the second experiment (N=34), we compare automatic and user-initiated communication of emoticons. Results show that user-initiated communication of emoticons is experienced as more intimate than automatic communication. These results are discussed in light of the interpersonal process model of intimacy and can help the design of applications aimed at improving social interactions through affective communication technology.     

基于层次结构的多策略中文微博情感分析和特征抽取

随着Web2.0时代的兴起,与微博相关的研究得到了学术界和工业界的广泛关注。该文使用新浪API获取数据,针对中文微博消息展开了情感分析方面的研究。我们对于三种情感分析的方法进行了深入研究,包括表情符号的规则方法、情感词典的规则方法、基于SVM的层次结构的多策略方法,实验表明基于SVM的层次结构多策略方法效果最好。其次,针对层次结构的多策略方法的特征选择进行了详细分析,包括主题无关、主题相关的特征。实验表明使用主题无关的特征时获得的准确率为66.467%。引入主题相关的特征后,准确率提升至67.283%。     

A blog emotion corpus for emotional expression analysis in Chinese

Weblogs are increasingly popular modes of communication and they are frequently used as mediums for emotional expression in the ever changing online world. This work uses blogs as object and data source for Chinese emotional expression analysis. First, a textual emotional expression space model is described, and based on this model, a relatively fine-grained annotation scheme is proposed for manual annotation of an emotion corpus. In document and paragraph levels, emotion category, emotion intensity, topic word and topic sentence are annotated. In sentence level, emotion category, emotion intensity, emotional keyword and phrase, degree word, negative word, conjunction, rhetoric, punctuation, objective or subjective, and emotion polarity are annotated. Then, using this corpus, we explore these linguistic expressions that indicate emotion in Chinese, and present a detailed data analysis on them, involving mixed emotions, independent emotion, emotion transfer, and analysis on words and rhetorics for emotional expression.     

Personalized Semantic Based Blog Retrieval

Blog retrieval is a complex task because of the informal language usage.Blogs deviate from the language which is used in traditional corpora largely due to various reasons.Spelling errors,grammatical irregularity,over use of abbreviations and symbolic characters like emotions are a few reasons of irregular corpus blogs.To make the retrieval of blogs easier,the novel idea of personalized semantic based blog retrieval(PSBBR) system is discussed in this paper.The blogs are tagged with a relationship to one another with reference to ontology.The meanings of the blog content and key term are tagged as XML tags.The query term accesses the XML tags to retrieve entire blog content.The system is evaluated with a huge number of blogs extracted from various blog sources.Relevance score is calculated for every blog associated with     

A novel approach for clustering sentiments in Chinese blogs based on graph similarity

Blog clustering is an important approach for online public opinion analysis. The traditional clustering methods, usually group blogs by keywords, stories and timeline, which usually ignore opinions and emotions expressed in the blog articles. In this paper, an integrated graph-based model for clustering Chinese blogs by embedded sentiments is proposed. A novel graph-based representation and the corresponding clustering algorithm are applied on the Chinese blog search results. The proposed model SoB-graph considers not only sentiment words but also structural information in blogs. Experimental results show that comparing with the traditional graph-based document representation model and vector space document representation model, the proposed SoB-graph model has achieved better performance in clustering sentiments in Chinese blog documents.