改进的CBOW情感信息获取研究

大数据时代,文本的情感倾向对于文本潜在价值挖掘具有重要意义,然而人工方法很难有效挖掘网络上评论文本的潜在价值,随着计算机技术的快速发展,这一问题得到了有效解决。在文本情感分析中,获取词语的情感信息对于情感分析至关重要,词向量方法一般仅对词语的语法语义进行建模,但是忽略了词语的情感信息,无法更好地进行情感分析。通过TF-IDF算法模型获得赋权矩阵,构建停用词表,同时根据赋权矩阵生成Huffman树作为改进的CBOW算法的输入,引入情感词典生成情感标签辅助词向量生成,使词向量具有情感信息。实验结果表明,提出的方法对评论文本中获得的词向量能够较好地表达情感信息,情感分类结果优于传统模型。因此,该模型在评论文本情感分析中可以有效提升文本情感分类效果。     

面向中文歌词的音乐情感分类方法

情感是音乐最重要的语义信息,音乐情感分类广泛应用于音乐检索,音乐推荐和音乐治疗等领域.传统的音乐情感分类大都是基于音频的,但基于现在的技术水平,很难从音频中提取出语义相关的音频特征.歌词文本中蕴含着一些情感信息,结合歌词进行音乐情感分类可以进一步提高分类性能.本文将面向中文歌词进行研究,构建一部合理的音乐情感词典是歌词情感分析的前提和基础,因此基于Word2Vec构建音乐领域的中文情感词典,并基于情感词加权和词性进行中文音乐情感分析.本文首先以VA情感模型为基础构建情感词表,采用Word2Vec中词语相似度计算的思想扩展情感词表,构建中文音乐情感词典,词典中包含每个词的情感类别和情感权值.然后,依照该词典获取情感词权值,构建基于TF-IDF （Term Frequency-Inverse Document Frequency）和词性的歌词文本的特征向量,最终实现音乐情感分类.实验结果表明所构建的音乐情感词典更适用于音乐领域,同时在构造特征向量时考虑词性的影响也可以提高准确率.     

一种基于情感依存元组的简单句情感判别方法

基于依存句法“动词配价”原理与组块的概念,提出以情感依存元组(EDT)作为中文情感表达的基本单位。它以句中能承载情感的几类实词作为中心词,修饰词依附于中心词,程度词和否定词依附于中心词和修饰词。该文对句子进行句法分析,在句法树和依赖关系中按规则提取情感依存元组,建立简单句情感依存元组判别模型计算情感倾向性。针对COAE2014评测公布的网络新闻语料,将该方法分别与有监督分类算法(KNN、SVM)和半监督算法(K-means)进行实验对比。结果表明,基于EDT的情感分类性能与有监督的机器学习算法相当,远高于半监督的聚类算法。     

基于情绪图片的PAD情感状态模型分析

针对情感计算领域的情感描述和测量问题,系统分析了情感的范畴观和维度观在情感计算领域中的意义。采用传统的心理测量方法,通过分析人们对330张图片在16个情绪维度上的评分,综合评价已有心理学研究中所涉及的众多维度,构建了2维和3维情感空间,指出了3个维度的本质、对人类情感信息的表达精度和相应的命名方法。实验结果表明,情绪范畴在3个情感空间中可以很好地分离开来,而且它们在情感空间中的分布可以非常直观地展示出各个基本情绪范畴之间的关系。这一结果证明3维情感空间可以充分地表达和量化人类情感,是情感计算研究的基础。     

基于连词的维吾尔语情感词库扩展研究

极性情感词是准确分析维吾尔文倾向性的基础资源。该文在前期构建的维吾尔语褒贬情感词典基础上进行网络情感词的自动扩展研究。首先分析维吾尔语情感表达的语言特征,总结了连词、程度副词与情感词的搭配规律,并基于此规律设计从情感语料库中获取候选情感词的算法,形成候选情感词库;最后再利用维吾尔语连词的特性,结合已创建的情感词典和维吾尔语反义词词典,以互联网作为超大规模语料库,设计基于搜索引擎的情感词极性判别算法,根据算法得分判别候选情感词的极性,再将其扩展到已构建的褒贬情感词库。实验结果表明,与扩展前的情感词库相比,使用互联网文本语料扩展后的情感词库后进行维吾尔语句子倾向性测评的准确率和召回率均有明显提高。     

基于扩展词典与语义规则的中文微博情感分析

首先分析微博文本新词出现规律,通过程度词发现微博新词,然后通过拓展的PMI算法,计算新词与情感基准词之间的点互信息值,根据点互信息值将新词分为褒贬2类后加入微博领域词典。接着构建基础情感词典,考虑到微博文本的独特性和汉语言特点,构建微博表情词典、否定词典、程度词词典、连词词典。最后结合情感词典与语义规则,通过与微博表情进行情感值加权的方式来对中文微博进行情感分析。通过对抓取的微博数据集进行测试,验证了本文提出的分析策略的有效性。     

文本情感倾向性分析

Aiming at the problem of manual annotation in the text sentiment analysis, a new method based on five tuple of appraisal expression is proposed. This  method just needs appropriate sentiment dictionary. The sentiment tendencies of comments are analyzed without lots of markup work. Through the combination of unsupervised and supervised learning methods to construct the evaluation thesaurus and evaluation object list; the extraction of appraisal expression is based on these lists, using linear chain conditional random fields model, which is in the chain of sentiment words. Finally, evaluation objects are divided into four categories and emotional words are divided into five types according to the relationship between semantic collocation, combined with the influence of sentence pattern, negative word and degree word on the sentiment analysis, a method of calculating the sentiment tendency of the text is put forward. Compared with other methods, this method based on the appraisal expression has obtained better F value, and it has a certain cross domain.     

Ontological reasoning for improving the treatment of emotions in text

With the advent of affective computing, the task of adequately identifying, representing and processing the emotional connotations of text has acquired importance. Two problems facing this task are addressed in this paper: the composition of sentence emotion from word emotion, and a representation of emotion that allows easy conversion between existing computational representations. The emotion of a sentence of text should be derived by composition of the emotions of the words in the sentence, but no method has been proposed so far to model this compositionality. Of the various existing approaches for representing emotions, some are better suited for some problems and some for others, but there is no easy way of converting from one to another. This paper presents a system that addresses these two problems by reasoning with two ontologies implemented with Semantic Web technologies: one designed to represent word dependency relations within a sentence, and one designed to represent emotions. The ontology of word dependency relies on roles to represent the way emotional contributions project over word dependencies. By applying automated classification of mark-up results in terms of the emotion ontology the system can interpret unrestricted input in terms of a restricted set of concepts for which particular rules are provided. The rules applied at the end of the process provide configuration parameters for a system for emotional voice synthesis.     

基于SVM的文本词句情感分析

近年来,文本情感倾向性分析已成为自然语言处理领域的热点,在垃圾过滤、文本分类、网络舆情分析等领域有广泛的应用。将研究中文文本词句的情感分析问题,重点解决喜、怒、哀、惧四类粒度大的情感分析问题。首先构建喜、怒、哀、惧基准情感词,然后对情感词特征进行分析,进而挖掘潜在情感词,最后使用支持向量机分类的方法融合词特征、词性特征、语义特征等各种特征,对句子进行情感识别及分类。实验表明,在COAE2009评测任务情感词句识别此方法是合理和有效的。     

面向微博的社会情绪词典构建及情绪分析方法研究

该文旨在探索一种面向微博的社会情绪词典构建方法,并将其应用于社会公共事件的情绪分析中。首先通过手工方法建立小规模的基准情绪词典,然后利用深度学习工具Word2vec对社会热点事件的微博语料通过增量式学习方法来扩展基准词典,并结合HowNet词典匹配和人工筛选生成最终的情绪词典。接下来,分别利用基于情绪词典和基于SVM的情绪方法对实验标注语料进行情绪分析,结果对比分析表明基于词典的情绪分析方法优于基于SVM的情绪分析方法,前者的平均准确率和召回率比后者分别高13.9%和1.5%。最后运用所构建的情绪词典对热点公共事件进行情绪分析,实验结果表明该方法是有效的。     

EmoTales: creating a corpus of folk tales with emotional annotations

Emotions are inherent to any human activity, including human–computer interactions, and that is the reason why recognizing emotions expressed in natural language is becoming a key feature for the design of more natural user interfaces. In order to obtain useful corpora for this purpose, the manual classification of texts according to their emotional content has been the technique most commonly used by the research community. The use of corpora is widespread in Natural Language Processing, and the existing corpora annotated with emotions support the development, training and evaluation of systems using this type of data. In this paper we present the development of an annotated corpus oriented to the narrative domain, called EmoTales, which uses two different approaches to represent emotional states: emotional categories and emotional dimensions. The corpus consists of a collection of 1,389 English sentences from 18 different folk tales, annotated by 36 different people. Our model of the corpus development process includes a post-processing stage performed after the annotation of the corpus, in which a reference value for each sentence was chosen by taking into account the tags assigned by annotators and some general knowledge about emotions, which is codified in an ontology. The whole process is presented in detail, and revels significant results regarding the corpus such as inter-annotator agreement, while discussing topics such as how human annotators deal with emotional content when performing their work, and presenting some ideas for the application of this corpus that may inspire the research community to develop new ways to annotate corpora using a large set of emotional tags.     

一种求解多执行模式资源水平问题的遗传算法

针对资源受限情况下多执行模式工程调度中资源水平问题的特点,设计了一种遗传算法。解的编码采用满足紧前关系的工作链表与工作执行模式链表结合的双链表结构,交叉算子采用修正的一点交叉算法。为保证收敛解的可行性,在适值函数计算时对不可行解进行惩罚。对标准问题库PSPLIB中大量问题的求解实验结果表明,遗传算法是求解该问题的一种有效算法。     

基于词向量的中文新词情感倾向性分析

为更具体表义社会新词的情感含义及其倾向性,该文提出了一种基于词向量的新词情感倾向性分析方法.在信息时代不断发展变化中,由于语言应用场景不断发展变化以及扩展语义表达的丰富性,网络上不断出现很多表达情感的新词,但是这些新词的表达虽有丰富的含义但缺乏准确的定义,因此对其情感倾向性分析具有一定困难.该文在分析了新词发现方法和词向量训练工具Word2Vec的基础上,研究了基于Word2Vec的情感词新词倾向性分析方法的可行性和架构设计,并面向微博语料进行实验,结果显示新词可以从与其相近的词中分析其情感倾向.     

Second-Order Cohesion

Similarity in contextual behavior between words is considered a source of 'lexical cohesion,' which is otherwise hard to measure or quantify. Such contextual similarity is used by an implementation for text segmentation, the VecTile system, which uses precompiled vector representations of words to produce similarity curves over texts. The performance of this system is shown to improve over that of the TextTiling algorithm of Hearst (1997).     

Multi-objective simulation optimization for uncertain resource assignment and job sequence in automated flexible job shop

In this study a multi-objective problem considering uncertainty and flexibility of job sequence in an automated flexible job shop (AFJS) is considered using manufacturing simulation. The AFJS production system is considered as a complex problem due to automatic elements requiring planning and optimization. Several solution approaches are proposed lately in different categories of meta-heuristics, combinatorial optimization and mathematically originated methods. This paper provides the metamodel using simulation optimization approach based on multi-objective efficiency. The proposed metamodel includes different general techniques and swarm intelligent technique to reach the optimum solution of uncertain resource assignment and job sequences in an AFJS. In order to show the efficiency and productivity of the proposed approach, various experimental scenarios are considered. Results show the optimal resources assignment and optimal job sequence which cause efficiency and productivity maximization. The makespan, number of late jobs, total flow time and total weighted flow time minimization have been resulted in an automated flexible job shop too.     

Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things (IoT). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus Ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.      

一种基于扩展的两步文本倾向性分析方法

提出一种基于扩展的两步文本倾向性分析方法,该方法利用包含倾向性词表、否定词表、程度词表在内的情感词语对训练文本进行特征扩展,按照将情感词语和内容词语是否同等对待来构造两个分类器CF1和CF2;在分类时,对测试文本进行和训练文本类似的特征扩展,使用分类器CF1对其进行分类,对分类结果中的可靠部分直接做出判定,对分类结果中的不可靠部分利用分类器CF2进行二次分类并做出判定。实验结果验证了该方法的有效性。     

加权维吾尔语句子倾向性分析

准确可靠的文本倾向性分析是网络舆情分析与网络内容安全的前提.本文提出了利用中文极性情感词典HowNet、NTUSD以及大连理工大学发布的褒贬情感词词典进行并交运算,选择并翻译为维吾尔语词汇,借助于维吾尔语同义近义词词典,扩展构建了维吾尔语极性情感词典;然后分析总结了否定词、程度副词以及句中的转折连词等情感修饰成分对维吾尔语句子情感极性的影响,并量化为情感词权值;最后设计了基于维吾尔语极性情感词和权值相结合的加权句子情感极性判定算法.利用自建语料库进行测试,并与汉语倾向性判定实验结果比较,证明了本算法进行维吾尔语句子褒贬情感性分析基本是有效地.     

情绪主导型群体事件的情绪演化模型与仿真

大规模群体负面情绪的形成与蔓延是引发情绪主导型群体事件的根本原因。考虑在社会关系网络中的个体是有限理性的情形下,对群体情绪形成原因进行分析,归纳了群体情绪感染规则与蔓延机制,并依此构建了群体情绪感染模型,利用多主体仿真平台Netlogo对群体负面情绪的演化过程进行仿真实验,考察不同情景下群体负面情绪的演变情况,结果表明普通民众的理性程度,个体间的情感关系,意见领袖的干预、占比、干预时间、情绪感染阈值等都对群体负面情绪有影响。最后,对情绪主导型群体突发事件的预防与对策给出了合理建议。     

基于规则的连用关系标记的自动标识研究

复句中的关系词对研究复句中各分句的语义关系有着重要意义,但在基于规则的关系词自动识别的研究中发现,并非复句中出现的关系标记都是关系词,从中识别出真正的关系词是研究的重点和难点。提出对一种典型的关系标记——位置相邻的关系标记进行自动标记的算法,该算法结合关系词库和关系词提取技术,分析其连用特征。实验表明,该算法对连用关系标记的标识准确率达到72.9%。