A cognitive emotion model enhanced sequential method for social emotion cause identification

Social emotion refers to the emotion evoked to the reader by a textual document. In contrast to the emotion cause extraction task which analyzes the cause of the author's sentiments based on the expressions in text, identifying the causes of social emotion evoked to the reader from text has not been explored previously. Social emotion mining and its cause analysis is not only an important research topic in Web-based social media analytics and text mining but also has a number of applications in multiple domains. As the focus of social emotion cause identification is on analyzing the causes of the reader's emotions elicited by a text that are not explicitly or implicitly expressed, it is a challenging task fundamentally different from the previous research. To tackle this, it also needs a deeper level understanding of the cognitive process underlying the inference of social emotion and its cause analysis. In this paper, we propose the new task of social emotion cause identification (SECI). Inspired by the cognitive structure of emotions (OCC) theory, we present a Cognitive Emotion model Enhanced Sequential (CogEES) method for SECI. Specifically, based on the implications of the OCC model, our method first establishes the correspondence between words/phrases in text and emotional dimensions identified in OCC and builds the emotional dimension lexicons with 1,676 distinct words/phrases. Then, our method utilizes lexicons information and discourse coherence for the semantic segmentation of document and the enhancement of clause representation learning. Finally, our method combines text segmentation and clause representation into a sequential model for cause clause prediction. We construct the SECI dataset for this new task and conduct experiments to evaluate CogEES. Our method outperforms the baselines and achieves over 10% F1 improvement on average, with better interpretability of the prediction results.     

A novel dropout mechanism with label extension schema toward text emotion classification

Researchers have been aware that emotion is not one-hot encoded in emotion-relevant classification tasks, and multiple emotions can coexist in a given sentence. Recently, several works have focused on leveraging a distribution label or a grayscale label of emotions in the classification model, which can enhance the one-hot label with additional information, such as the intensity of other emotions and the correlation between emotions. Such an approach has been proven effective in alleviating the overfitting problem and improving the model robustness by introducing a distribution learning component in the objective function. However, the effect of distribution learning cannot be fully unfolded as it can reduce the model’s discriminative ability within similar emotion categories. For example, “Sad” and “Fear” are both negative emotions. To address such a problem, we proposed a novel emotion extension scheme in the prior work (Li, Chen, Xie, Li, and Tao, 2021). The prior work incorporated fine-grained emotion concepts to build an extended label space, where a mapping function between coarse-grained emotion categories and fine-grained emotion concepts was identified. For example, sentences labeled “Joy” can convey various emotions such as enjoy, free, and leisure. The model can further benefit from the extended space by extracting dependency within fine-grained emotions when yielding predictions in the original label space. The prior work has shown that it is more apt to apply distribution learning in the extended label space than in the original space. A novel sparse connection method, i.e., Leaky Dropout, is proposed in this paper to refine the dependency-extraction step, which further improves the classification performance. In addition to the multiclass emotion classification task, we extensively experimented on sentiment analysis and multilabel emotion prediction tasks to investigate the effectiveness and generality of the label extension schema.     

突发事件背景下内容添加型转发微博的情绪与认知变化研究

【 目的/意义】转发行为是社交媒体上信息传播的重要方式,转发者添加的评论能够反映情绪状态与认知情 况,研究突发自然灾害事件背景下的转发评论有助于应急管理部门了解公众情绪走向和认知变化。【方法/过程】转 发型微博被再次转发时会产生转发级联并形成不同转发层级,本文利用LIWC软件从情绪和认知角度分析转发者 添加的评论内容,对比不同层级情绪得分和认知得分的差异以及转发评论中情绪和认知变化。【结果/结论】突发自 然灾害相关原始微博和转发评论的情绪和认知具有显著差异,用户在原始微博中倾向使用关于积极情绪、消极情 绪、洞察和原因的词;在不同转发层级中,表达积极情绪、洞察和矛盾的词的使用频率更高,且具有更大的波动;在 转发评论过程中,相同类型的情绪和认知显著正相关。【创新/局限】本文研究了转发者生成内容的特征,为应急管 理部门的灾后决策和舆情治理提供新视角,没有考虑添加评论对微博转发的影响,后续将对比不同转发微博的传 播效果。     

基于主题的微博情绪与传播力关系研究——以"中美贸易战"为例

【Purpose/significance】this paper crawls the microblog related to the "Sino-US trade war" event, aiming to clari⁃ fy the relationship between the emotional characteristics and communication power of microblogs on different themes in the event, which is helpful to control and guide the public opinion.【Method/process】Firstly, we extract the topic of microblog data, classify the data into different emotion and calculate the emotional intensity, then we study distribution of emotion types,and discuss the relationship between the emotional types and the emotional intensity and the communication power of each topic.【Results/conclusion】The results show that: ①The analysis of the proportion and intensity of emotions of dif⁃ ferent themes can effectively identify the themes and emotions with potential risks. ②The analysis of the relationship be⁃ tween high proportion and high intensity of emotion and communication power in different themes can provide reference for formulating emotion guidance strategies.     

邻避事件中的公众情绪、政府回应与信息转发意愿研究

[目的/意义]邻避事件中,非利益相关公众在网络空间"借机"宣泄负性情绪而导致的舆情,可能放大当地利益相关公众的负性情绪和风险感知,致使邻避事件的冲突激化。对非利益相关公众这种负性情绪在政府回应前后的变化及其与信息转发意愿的关系进行探讨,是网络时代舆情治理的重要议题,但目前鲜有研究涉及。[方法/过程]采用2(情绪性:高负性情绪、低负性情绪)×2(回应透明度:高透明、低透明)组间设计,以独立样本T检验比较政府回应对个体负性情绪变化状况的影响,同时通过回归分析探讨个体的负性情绪在政府回应前后对其信息转发意愿的影响以及政府信任在其中的调节作用。[结果/结论]研究表明:在政府未回应时,非利益相关公众的负性情绪感受越高,对非官方信息的转发意愿越强,而在政府回应后,负性情绪感受越低,对官方回应信息的转发意愿越强;政府未回应时,对政府的信任能够减弱负性情绪对非官方信息转发意愿的正向影响,政府回应后,对政府的信任能够增强负性情绪对官方回应信息转发意愿的负向影响;政府回应尤其是高透明的回应,能够显著降低非利益相关公众的负性情绪感受。     

任务紧迫性下项目创新行为实验研究——基于情绪的调节作用

任务紧迫性是项目管理中面临的常见问题,通过情景实验,模拟了时间限制下项目执行者的感知时间压力、情绪与创新行为交互作用的主要过程。实验结果说明,在项目实施过程中时间限制会加大项目执行者的感知时间压力并产生消极情绪,进而对创新行为带来不利影响;积极情绪的个体在面对较高的感知时间压力时趋向于选择规避风险较大的创新行为;积极的情绪在调节感知时间压力和创新行为之间起到正向调节作用,而外界的消极情绪极易对员工积极情绪带来负面影响。项目管理者应该结合任务的创新特征综合确定缓冲设置方案,重视压力管理和积极情绪的诱导,通过创新氛围的构建诱导员工积极投身创新活动。     

亲社会行为对员工创新绩效的双刃剑效应——基于情绪的中介作用及领导-成员交换关系的调节作用

本研究结合工作要求-资源模型和领导-成员交换理论,基于607份在职员工的多时段配对数据,构建亲社会行为通过影响自身情绪资源的消耗及获取,进而影响员工创新绩效的理论模型,并验证了领导-成员交换关系在这一过程中的作用。研究发现：实施亲社会行为会获得情绪资源从而产生积极情绪,但其也会消耗情绪资源使员工感到情绪耗竭；积极情绪和情绪耗竭中介亲社会行为对员工创新绩效的影响；领导-成员交换关系能够调节亲社会行为与情绪之间的关系强度。     

情绪与认知的脑机制研究进展

情绪是复杂的心理生理学现象,反映了心智状态与个体内在的生物化学系统和外部环境影响的相互作用。情绪的识别、产生和控制主要依赖于腹侧系统和背侧系统的功能整合。对情绪的遗传机制研究有助于人们在理论上加深对情绪本质的理解,在应用上进行情绪障碍的预防与干预。情绪与认知是相互依存、相互作用的,具体反映在情绪与注意、工作记忆和决策等方面的关系。焦虑症和抑郁症作为情绪障碍的代表,具有重要的临床研究价值。     

An analysis of cognitive change in online mental health communities: A textual data analysis based on post replies of support seekers

The replies of people seeking support in online mental health communities can be analyzed to discover if they feel better after receiving support; feeling better indicates a cognitive change. Most research uses key phrase matching and word frequency statistics to identify psychological cognitive change, methods that result in omissions and inaccuracy. This study constructs an intelligent method for identifying psychological cognitive change based on natural language processing technology. It incorporates information related to emotions that appears in reply text to help identify whether psychological cognitive change has occurred. The model first encodes the emotion information based on rule matching and manual annotation, then adds the encoded emotion lexicon and a cognitive change lexicon to a word2vec high-dimensional semantic word vector training, converts the annotated cognitive change recognition text into a vector matrix using the trained model, and train in the annotated text using TextCNN. To compare the results with those of the traditional methods (key phrase matching and sentiment word frequency statistics), this study uses a semi-automated approach to construct a lexicon of psychological cognitive change, as well as a keyword lexicon without cognitive change, based on word vectors and similarity. We compare the performance of the classifier before and after the fusion of the graphical emotion information, compare the LSTM and Transformer as baselines, and compare traditional word frequency statistics methods. The experimental results show that our proposed classification model performs better than the others; it achieves 84.38% precision, an 84.09% recall rate, and an 84.17% F1 value. Our work bears methodological implications for online mental health platforms.     

Does government information release really matter in regulating contagion-evolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics

The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergencies-oriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.     

Dual emotion based fake news detection: A deep attention-weight update approach

The proliferation of false information is a growing problem in today's dynamic online environment. This phenomenon requires automated detection of fake news to reduce its harmful effect on society. Even though various methods are used to detect fake news, most methods only consider data-oriented text features; ignoring dual emotion features (publisher emotions and social emotions) and thus lack higher levels of accuracy. This study addresses this issue by utilizing dual emotion features to detect fake news. The study proposes a Deep Normalized Attention-based mechanism for enriched extraction of dual emotion features and an Adaptive Genetic Weight Update-Random Forest (AGWu-RF) for classification. First, the deep normalized attention-based mechanism incorporates BiGRU, which improves feature value by extracting long-range context information to eliminate gradient explosion issues. The genetic weight for the model is adjusted to RF and updated to achieve optimized hyper parameter values ​​that support the classifiers' detection accuracy. The proposed model outperforms baseline methods on standard benchmark metrics in three real-world datasets. It outperforms state-of-the-art approaches by 5%, 11%, and 14% in terms of accuracy, highlighting the significance of dual emotion capabilities and optimizations in improving fake news detection.     

Tracking and recognizing emotions in short text messages from online chatting services

To automate the process of emotion recognition, in this study, we develop a computational approach for continuously tracking and analyzing users’ emotions while chatting online. Our work has several unique features: it provides relative probabilities of possible emotions for a word, constructs a distribution for each chatting message accordingly, performs a clustering procedure for the message distribution, and aggregates the emotions of continuous chatting sentences to draw the conclusion. To evaluate the proposed approach, we conducted experiments in two phases. The first phase was to evaluate the effectiveness of the proposed computational approach in analyzing the chatting sentences. The participants were asked to focus on tagging emotions toward each sentence for a pre-designed dialogue. The second phase involves a real-time chatting between two online users. The participants were asked to choose topics and freely chat with each other. The messages were analyzed, and the results were provided to the users for their evaluations. The results show that our approach is both effective and efficient in tracking the emotions of chatting users. Additional analyses and further discussions were carried out to further evaluate the quantitative experimental results. All the findings confirmed the usefulness and feasibility of the presented approach.     

微博社区网民情绪引导与舆情控制的SIR演化博弈分析

【目的/意义】通过研究微博社区网民的情感交互与舆情观点,有助于在复杂的网络中掌握网民情绪演化从而良性引导网络舆情态势。【方法/过程】基于传播动力学、社会安全阀等理论,结合微博社区中的实际案例,定义事件利益主体并抽象出事件演化的全生命周期,同时,构建SIR演化博弈模型刻画网民情绪的动态演化规律及主体决策博弈演化过程,并通过仿真模拟分析得到系统演化至稳定状态的均衡条件。【结果/结论】结果表明:微博社区中意见领袖与官方媒体感知收益与风险的敏感度对决策行为产生显著影响;官方媒体及时设置有效议程构建安全阀能够防止网民情绪恶化;意见领袖与官方媒体的协同引导能够最大效度地帮助政府管控网络舆情。     

浅析大学生述情障碍的成因

述情障碍,也称作情感表达不能,以不能恰当表达自身的情绪和情感、鉴别情绪困难和人际关系不良为典型特征。大学生述情障碍常被认为与人际关系僵化、抑郁和心理健康显著相关。本文综合了社会机制和认知机制两种视角对大学生述情障碍的成因、影响因素以及干预措施进行探讨,希望能为开展高校大学生心理健康教育及咨询工作提供参考。     

Examining the relationship between specific negative emotions and the perceived helpfulness of online reviews

This paper extracted discrete emotions from online reviews based on an emotion classification approach, and examined the differential effects of three discrete emotions (anger, fear, sadness) on perceived review helpfulness. We empirically tested the hypotheses by analyzing the “verified purchase” reviews on Amazon.com. The findings of this study extend the previous research by suggesting that product type moderates the effects of emotions on perceived review helpfulness. Anger embedded in a customer review exerts a greater negative impact on perceived review helpfulness for experience goods than for search goods. Fear embedded in a review is identified as an important emotional cue to positively affect the perceived review helpfulness with more persuasive messages. As the level of sadness embedded in a review increases, perceived review helpfulness decreases. These findings contribute to a better understanding of the important role of emotions embedded in reviews on the perceived review helpfulness. This study also provides practical insights related to the presentation of online reviews and gives suggestions for consumers regarding how to select and write a helpful review.     

Examining the Influence of Emotional Expressions in Online Consumer Reviews on Perceived Helpfulness

Drawing on the cognitive appraisal theory of emotions and the attribution theory, this study extends existing research by examining how the emotional expressions influence perceived helpfulness of online consumer reviews (OCRs). We include two negative emotions (anger, fear) and two positive emotions (pride, surprise). Each of these four emotions can be described with respect to six emotional appraisal dimensions which containing certainty, pleasantness, attentional activity, anticipated effort, control and others’ responsibility. Hypotheses thus developed were empirically validated using the laboratory experiment in the context of restaurant services. Research results indicated that emotion expressions in OCRs have an indirect effect on perceived helpfulness through attribution about the reviewer's cognitive effort. We find that reviews with negative emotions are perceived to be more reviewer's cognitive efforts than positive emotions. More specifically, OCRs with negative emotions tend to comprise more diagnostic features related the product or service, and are more informative. We further examined whether the gender of reader moderates the relationship between different emotional expressions and perceived reviewer's cognitive effort. The results find that reviews conveying positive emotions tended to have a greater impact on male readers’ perception of reviewer's cognitive effort than those of female readers. Reviews conveying negative emotions were found to have a greater impact on female readers’ perception of reviewer's cognitive effort than that of male readers. The study results add to existing knowledge of the influence of emotional expression on perceived helpfulness, which will advance our understanding of information processing in the psychological mechanisms influencing the attitude. Applying the results from this study, restaurant service providers can make different coping strategy for discrete emotions and platform administrators can assistant reviewer express their emotions more precisely.     

运用合理情绪疗法改善高中生人际交往障碍的个案研究

本案例运用合理情绪疗法为一名因人际交往出现情绪困扰的高中生来访者进行了心理咨询。针对其不合理的信念和认知模式采取认知行为矫正,引导其进行认知训练以形成合理的理性认知,从而缓解不良情绪,最终达到咨询效果。     

浅谈中职美术教学中的情感教育

新课程标准中明确指出,美术教学中最为重要的特性包括情感性。在中职美术教学中积极渗透情感教育有利于激发学生美术学习的积极性,同时也有利于学生在美术创作时生动准确表达出自身的情感。本文主要结合中职美术教育的实际,提出了在美术教学中渗透情感教育的几项措施。     

Studying emotion induced by music through a crowdsourcing game

One of the major reasons why people find music so enjoyable is its emotional impact. Creating emotion-based playlists is a natural way of organizing music. The usability of online music streaming services could be greatly improved by developing emotion-based access methods, and automatic music emotion recognition (MER) is the most quick and feasible way of achieving it. When resorting to music for emotional regulation purposes, users are interested in the MER method to predict their induced, or felt emotion. The progress of MER in this area is impeded by the absence of publicly accessible ground-truth data on musically induced emotion. Also, there is no consensus on the question which emotional model best fits the demands of the users and can provide an unambiguous linguistic framework to describe musical emotions. In this paper we address these problems by creating a sizeable publicly available dataset of 400 musical excerpts from four genres annotated with induced emotion. We collected the data using an online “game with a purpose” Emotify, which attracted a big and varied sample of participants. We employed a nine item domain-specific emotional model GEMS (Geneva Emotional Music Scale). In this paper we analyze the collected data and report agreement of participants on different categories of GEMS. We also analyze influence of extra-musical factors on induced emotion (gender, mood, music preferences). We suggest that modifications in GEMS model are necessary.