Affective Words Research Articles

EXPRESS: Exploring the differences in processing between Chinese emotion and emotion-laden words: A cross-task comparison study.

Affective words can be classified into two types: emotion words (EM words, e.g., "happy") and emotion-laden words (EL words, e.g., "wedding"). Several studies have shown differences in processing between EM and EL words, although results are inconclusive. These two types of words may have representational differences because affective content is an inherent part of the semantic features of EM words (i.e., denotative meaning) but not of EL words, whose affective content is part of their connotative meaning (i.e., these words do not name emotions, but are associated to emotions). In this study, we tested a set of Chinese EM and EL words. Both conditions included positive and negative words. The study involved two tasks, an implicit task, in which emotional content was not relevant (lexical decision task, LDT), and an explicit task, in which the emotional content was relevant (affective categorization task, ACT). Our results showed that participants responded faster to EM words than to EL words. This advantage was mostly observed in the ACT and with negative words. These results reveal differences in processing between EM and EL words which can be related to the greater relevance of affective content in the meaning of EM words compared to EL words.

Can Social Technologies Drive Purchases in E-Commerce Live Streaming? An Empirical Study of Broadcasters’ Cognitive and Affective Social Call-to-Actions

As an emerging social technology, live streaming has facilitated a synchronous and interactive selling setting for e-commerce sellers and consumers. Despite growing operations management (OM) in e-commerce live streaming (ELS), the prior literature has largely neglected the directive social operations of ELS broadcasters, which are crucial to a company's business operations and marketing strategies. To mitigate this gap, we investigated the effects of social call-to-actions (SCTAs), an ELS-specific directive social operation, on consumer purchase outcomes in ELS. To explicate the mechanisms driving the purchase effect of SCTAs, we draw upon the notions of cognitive and affective marketing appeals to categorize SCTAs into two types: cognitive and affective SCTAs. We measure broadcasters’ SCTAs from ELS speech-to-text data using text-mining techniques and adopt econometric model estimations via an instrumental variables identification approach to quantify the relationships between ELS broadcasters’ SCTAs and consumer purchases. Our results uncover a significant positive purchase effect of affective SCTAs but an insignificant effect of cognitive SCTAs on product-level purchase orders in ELS. Specifically, a one-unit increase in affective SCTA-related words per minute is related to increased purchase orders of a product by 0.43%, i.e., averaging a 1.10% growth in demand per minute. Additionally, we find that the effects of cognitive and affective SCTAs are contingent on product and broadcaster types. Our session-level mediation effect analysis verifies the underlying mechanisms that drive the purchase effects of broadcasters’ SCTAs. Specifically, we find that cognitive SCTAs impede social engagements and thereby purchases, whereas affective SCTAs can boost social engagements, leading to increased session traffic and ultimately product sales in ELS. Our study provides novel empirical findings and important practical implications for ELS platforms and broadcasters.

Emotion word repertoire in the adult attachment interview is linked to reduced suicide attempts in patients with borderline personality disorder

BackgroundThis study explored the association between emotion word repertoire (EWR), attachment, reflective functioning and personality organization (PO) and suicidal behavior in borderline personality disorder (BPD) patients. MethodsThe current study performed a secondary data analysis from a randomized control trial for BPD patients (all female; n = 87; age: m = 27; SD = 7.42). EWR was assessed via machine-scoring transcripts of Adult Attachment Interviews (AAI) for affective words using the VETA electronic scoring software for the Levels of Emotional Awareness Scale (LEAS). Generated scores were related to impairments in PO (Structured Interview for Personality Organization; STIPO), attachment organization (AAI) and mentalization (Reflective Functioning Scale), general symptom severity (Brief Symptom Inventory; BSI-53), self-harm and suicidal behavior. Independent effects of the investigated predictors were studied using Bayesian path analysis. ResultsCorrected for education, findings in Bayesian path analysis suggest an independent negative association between EWR and suicide attempts (BE = −.32; 95 % CI [-.51, −.12]) and positive associations of deficits in PO with psychiatric symptoms (BE = .23; 95 % CI [.01, .44]) as well as suicide attempts (BE = .30; 95 % CI [.08, .49]). DiscussionThe findings underscore the potential role of high EWR and PO as a protective factor for suicidal behavior in individuals with BPD.

Multimodal Emotion Cognition Method Based on Multi-Channel Graphic Interaction

The relationship between the emotional components associated with images and text is a crucial way of multimodal emotion analysis. However, most of the present multimodel affective cognitive models simply associate the features of images and texts without thoroughly investigating their interactions, resulting in poor recognition. Therefore, a multimodel emotion cognition method based on multi-channel graphic interaction is proposed. Text context features are extracted, scene and image information is encoded, and useful features are obtained. Based on these results, the modal alignment module be applied to obtain information about affective regions and words, and then the cross-modal gating module be applied to combine the multimodel features. In addition, we tested extensively on three open datasets, achieving an accuracy of 0.8122 for the MSA-single dataset, 0.7307 for the MSA-MULTIPLE dataset, and 0.7159 for TumEmo. The results show that this method is effective for multimodal emotion detection.

Statistical Relationships Between Phonological Form, Emotional Valence and Arousal of Spanish Words.

A number of studies have provided evidence of limited non-arbitrary associations between the phonological forms and meanings of affective words, a finding referred to as affective sound symbolism. Here, we explored whether the affective connotations of Spanish words might have more extensive statistical relationships with phonological/phonetic features, or affective form typicality. After eliminating words with poor affective rating agreement and morphophonological redundancies (e.g., negating prefixes), we found evidence of significant form typicality for emotional valence, emotionality, and arousal in a large sample of monosyllabic and polysyllabic words. These affective form-meaning mappings remained significant even when controlling for a range of lexico-semantic variables. We show that affective variables and their corresponding form typicality measures are able to significantly predict lexical decision performance using a megastudy dataset. Overall, our findings provide new evidence that affective form typicality is a statistical property of the Spanish lexicon.

Using affective content to promote high-involvement services on social media

Service providers’ communication on social media has become a viable method to influence customer purchasing behavior and firm outcomes. Because services are intangible, one of the most pertinent challenges is to design text-based social media content to reduce customers’ perceived risk and enhance desired outcomes. According to Emotions as Social Information (EASI) theory, affective expression can positively influence observer’s reactions. Yet, evidence suggests that affective content (i.e., the use of affective words) is less helpful in high-involvement situations, as customers prefer cognitive information to reduce risk. However, four experiments reveal that high-involvement service providers can enhance customers’ purchase intentions by employing affective content in their online communication. This is because affective content signals effort of the provider, reducing perceived risk, and increasing purchase intentions. Results also demonstrate affective content works better for prevention- (vs. promotion) focused customers and for providers with high-quality reputations, indicating the relative primacy of inferential over affective processes when evaluating affective content. Practically, service providers should carefully rebalance their communication to increase affective content in social media posts.

Understanding latent affective bias in large pre-trained neural language models

Groundbreaking inventions and highly significant performance improvements in deep learning based Natural Language Processing are witnessed through the development of transformer based large Pre-trained Language Models (PLMs). The wide availability of unlabeled data within human generated data deluge along with self-supervised learning strategy helps to accelerate the success of large PLMs in language generation, language understanding, etc. But at the same time, latent historical bias/unfairness in human minds towards a particular gender, race, etc., encoded unintentionally/intentionally into the corpora harms and questions the utility and efficacy of large PLMs in many real-world applications, particularly for the protected groups. In this paper, we present an extensive investigation towards understanding the existence of “Affective Bias” in large PLMs to unveil any biased association of emotions such as anger, fear, joy, etc., towards a particular gender, race or religion with respect to the downstream task of textual emotion detection. We conduct our exploration of affective bias from the very initial stage of corpus level affective bias analysis by searching for imbalanced distribution of affective words within a domain, in large scale corpora that are used to pre-train and fine-tune PLMs. Later, to quantify affective bias in model predictions, we perform an extensive set of class-based and intensity-based evaluations using various bias evaluation corpora. Our results show the existence of statistically significant affective bias in the PLM based emotion detection systems, indicating biased association of certain emotions towards a particular gender, race, and religion.

Affective norms for 501 Chinese words from three emotional dimensions rated by depressive disorder patients.

Emotional words are often used as stimulus material to explore the cognitive and emotional characteristics of individuals with depressive disorder, while normal individuals mostly rate the scores of affective words. Given that individuals with depressive disorder exhibit a negative cognitive bias, it is possible that their depressive state could influence the ratings of affective words. To enhance the validity of the stimulus material, we specifically recruited patients with depression to provide these ratings. This study provided subjective ratings for 501 Chinese affective norms, incorporating 167 negative words selected from depressive disorder patients' Sino Weibo blogs, and 167 neutral words and 167 positive words selected from the Chinese Affective Word System. The norms are based on the assessments made by 91 patients with depressive disorder and 92 normal individuals, by using the paper-and-pencil quiz on a 9-point scale. Regardless of the group, the results show high reliability and validity. We identified group differences in three dimensions: valence, arousal, and self-relevance: the depression group rated negative words higher, but positive and neutral words lower than the normal control group. The emotional perception affected the individual's perception of words, to some extent, this database expanded the ratings and provided a reference for exploring norms for individuals with different emotional states.

Linguistic analysis of health anxiety during the COVID-19 pandemic.

Health anxiety, which is defined as fear of having or contracting serious physical illness, is particularly salient in light of the COVID-19 pandemic. We conducted a mixed methods study in which 578 narrative samples were analyzed using Linguistic Inquiry and Word Count (LIWC) software to determine linguistic markers from six LIWC categories relevant to cognitive-behavioral features of health anxiety. Broad linguistic predictors were analyzed through three backward elimination regression models in order to inform subcategory predictors of each area of health anxiety. Thus, both broad and specific linguistic predictors of general health anxiety, virus-relevant body vigilance, and fears of viral contamination were examined. Greater use of affective category words in written narratives predicted general health anxiety, as well as body vigilance and viral contamination fears. These findings represent the first direct demonstration of linguistic analysis of health anxiety and provide nuanced information about the nature and etiology of health anxiety.

Assessing the Impact of Static, Contextual and Character Embeddings for Arabic Machine Translation

Word embeddings/representations are an important component of Natural Language Processing (NLP) tasks. Most Neural Machine Translation (NMT) systems that use such representations disregard word morphology by assigning a unique vector to each unique word in the used vocabulary and thus cannot handle Out-Of-Vocabulary (OOV) words. In some languages, such as Arabic, the meaning of words is associated with the meaning of the individual characters that constitute them, as these characters embody internal information. In this study, a combination of character- and word- level models is used to determine the most effective approaches to semantically and morphologically representing affective Arabic words. Furthermore, this work examines the strategy of combining static, character and contextual word embeddings to obtain richer representations for the Arabic Machine Translation (MT) task. To the best of our knowledge, we are the first to investigate the combination of static word embeddings, contextual ones and character-level representation in Arabic MT. Furthermore, a Deep Learning (DL) architecture is employed on data preprocessed by various prominent preprocessing techniques. Various experiments were conducted and the findings indicate that the integration of various models for word embedding and character-level representation is feasible and more effective than the state-of-the-art Arabic MT systems.

What distinguishes emotion-label words from emotion-laden words? The characterization of affective meaning from a multi-componential conception of emotions.

Past research that distinguishes between affective and neutral words has predominantly relied on two-dimensional models of emotion focused on valence and arousal. However, these two dimensions cannot differentiate between emotion-label words (e.g., fear) and emotion-laden words (e.g., death). In the current study, we aimed to determine the unique affective characteristics that differentiate emotion-label, emotion-laden, and neutral words. Therefore, apart from valence and arousal, we considered different affective features of multi-componential models of emotion: action, assessment, expression, feeling, and interoception. The study materials included 800 Spanish words (104 emotion-label words, 340 emotion-laden words, and 356 neutral words). To examine the differences between each word type, we carried out a Principal Component Analysis and a Random Forest Classifier technique. Our results indicate that these words are characterized more precisely when the two-dimensional approach is combined with multi-componential models. Specifically, our analyses revealed that feeling, interoception and valence are key features in accurately differentiating between emotion-label, emotion-laden, and neutral words.

Emotional congruency between faces and words benefits emotional judgments in women: An event-related potential study

The present study aimed to investigate the effects of emotional congruency between faces and words on word evaluation through event-related brain potentials (ERPs). To this end, 20 women performed a face-word congruency task in which an emotional face was presented simultaneously with an affective word in a non-superimposed format. Participants had to evaluate the emotional valence of the word in three different conditions: congruent, incongruent, and control. The emotionally congruent words were categorized faster and more accurately than the incongruent ones. In addition, the emotionally congruent words elicited higher P3/LPP amplitudes than the incongruent ones. These results indicate a beneficial effect of emotional face-word congruency on emotional judgments of words.

An Emotional Analysis of Korean Topics based on Social Media Big Data Clustering

An innovative approach is introduced in this paper to address the challenges in emotional topic interpretation and accuracy in emotional situation assessment. Utilizing large data from social media to improve the accuracy of emotional analysis in online debates, with a specific emphasis on Korean themes. The proposed solution, the Online Topic Emotion Recognition Model (OTSRM), builds upon the foundational Online Latent Dirichlet Allocation (OLDA) model. The OTSRM integrates the concept of emotion intensity and introduces an inventive emotion iteration framework to tackle these issues. Key innovations of the OTSRM include establishing an affective evolution channel by augmenting affective heritability using a β priori. Additionally, the model generates two critical distribution matrices: one for characteristic words and another for affective words, facilitating a deeper understanding of emotional context within topics. The relative entropy method is employed to discern emotional tones in textual content, calculating maximum emotion values for topic focus within adjacent time segments. Validation experiments using five diverse network event datasets and comparisons to mainstream models demonstrate the OTSRM's effectiveness with emotion recognition accuracy rates of 85.56% and 81.03%. The OTSRM represents significant progress in addressing challenges associated with emotional topic analysis and precise emotional dynamics assessment in Korean social media data.

A sentiment analysis method for COVID-19 network comments integrated with semantic concept

In recent years, the new coronavirus COVID-19 has brought great disaster and loss to the world and is still spreading around the world. The situation in China is generally well controlled, and the lockdown has been removed, but the comments and messages about the epidemic persist online. For people working and living normally in China, their attitudes and views toward COVID-19 directly reflect the current situation of the pandemic. This paper collected Chinese microblogs, forums, and online comments, identified the latest comments about COVID-19, and conducted a sentiment analysis of them. Specifically, we proposed a new sentiment analysis method that integrated the semantics of words with the text analyzed. Different from the traditional sentiment analysis method which only relied on sentiment words, the proposed method extended the semantic concepts of affective words by integrating the semantic conceptual information about the affective words from the context of the comments and thus, provided information to support the final judgment of the affective opinions. The proposed approach incorporated the part-of-speech embedding information along with word embedding and relies on semantic concepts to enhance the emotional expression of words in context. The experimental results showed that by integrating the semantics of words, the accuracy of sentiment analysis is substantially improved, and it also reflected that different semantics of the same word have different influences on sentiment analysis. On several benchmark datasets, there was a 3–6% improvement in accuracy.

60 Recognition of Emotional Words: Relationship Between Rumination, Depression, Objective and Subjective Cognitive Impairment

Objective:Cognitive impairment in depression could present as subjective and objective, but the intensity of subjective impairment is higher and is not correlated with the deficits measured by neuropsychological tests. Subjective cognitive impairment lowers quality of life and is associated with the severity of depression. Among depressive patients, negative emotional bias is present. It is better memory for negative or positive information than for neutral information. We hypothesized that rumination is associated with subjective cognitive impairment.Participants and Methods:The study was performed through the online PsyToolkit platform. The study sample consisted of 168 healthy controls and 93 patients with depression were enrolled in the study. Participants completed questionnaires and performed a memory task that contained emotional words. The forty words were chosen from the Nencki Affective Word List. The list for memory test consists of 5 words from each Category: Happiness, Anger, Fear, Sad, Disgusting, and Neutral, and 10 words from category ‘Unclassified’ to balance the valence and arousal of the set. After 15 minutes, they recognized old words from the list of 80 words. Half of them were the same as in the first part, and other words were new, chosen with the same criteria. Depression symptoms were measured using the Beck Depression Inventory-II, subjective cognitive impairment with the Perceived Deficits Questionnaire-20, and intensity of rumination with the Polish Questionnaire of Rumination.Results:Subjective cognitive impairment and rumination were higher in patients with depression. They also had a higher error rate than healthy controls U = 6462.5; p = .021, especially in words from the neutral (U = 6292; p = .008) and happiness category (U = 6585; p = .031). However, there was no association between subjective cognitive impairment and performance in memory tasks. The regression analysis showed that depressive symptoms and intensity of rumination are better predictors of subjective cognitive deficits than objective performance on a memory task. The results of PDQ-20 were correlated with the error rate of words from categories: Happiness (r = -.162**), Sad (r = -.116*) and Neutral (r = -.145**). Words from categories Happiness, Disgust and Neutral were significantly better recognized than Unclassified, Fear or Sad group of words χ 2 F (6) = 132.685; p < .001. In MDD group recognition of Disgust words were statistically highest than Anger and Fear words χ 2 F (6) = 36.795; p < .001. The recognition of Happiness and Neutral word were not significantly different to other words.Conclusions:Cognitive problems are common in depression and affect quality of life. Subjective cognitive impairment is more closely related to rumination than objective cognitive impairment among healthy participants and patients with major depressive disorder. Emotional bias among depressive patients has been partially confirmed. Emotional impact is important for memory of words and subjective perception of cognition.

Application of attentional bias modification to reduce attentional bias and emotional reactivity to stress in mildly depressed individuals.

This study aims to verify the effectiveness of attentional bias modification (ABM) in reducing attentional bias related to depression, particularly in the later stages of attention as a pattern of difficulty in disengagement from depression-relevant stimuli, and to assess its effects on emotional reactivity to stress. A total of 78 participants were separated into four groups based on their levels of depression (minimal and mild) and the types of ABM. The positive ABM (pABM) trained participants to disengage their attention from depression-relevant stimuli and directed their attention toward more positive stimuli, whereas the neutral ABM (nABM) was designed to have no effect. The participants underwent a free-viewing task by eye tracker both before and after ABM to observe changes in attentional bias. Subsequently, they reported their emotional response after a stress-inducing task. The group of mildly depressed participants receiving pABM showed significantly less attention to depression-relevant negative affective stimuli and reported significantly decreased negative emotional reactivity to stress compared to the other groups. pABM had an effect on decreasing difficulty in disengaging from depression-relevant negative affective words (DW). However, it did not increase the dwell time on positive affective words (PW) in the current study. This might be due to the short duration of the application of ABM. The current study conducted ABM twice in 1 day, and this might not be enough to increase the dwell time on PA. This study verified that the ABM effectively decreased the attentional bias of depression and its relevant symptom, emotional reactivity to stress.

Functional fantasies: the regulatory role of grandiose fantasizing in pathological narcissism.

Pathological narcissism is characterized by maladaptive efforts to maintain a bolstered but fragile sense of self. Clinical theory suggests that grandiose fantasizing may be one form of this self-regulation. However, no empirical research has directly assessed the regulatory function of grandiose fantasizing in narcissism. Here, we examine (1) whether people scoring higher in narcissism choose to engage in grandiose fantasizing to regulate themselves when they are feeling down and (2) whether grandiose fantasizing is a more efficacious self-esteem and affect regulator for people scoring higher in narcissism than it is for those scoring lower in narcissism. Adult participants (N = 189) completed a self-report measure of narcissism and were randomized to either a negative mood induction or filler task condition. Then, participants wrote about a future event to make themselves feel better, choosing between a positive affect word or a grandiose word to guide their writing. Throughout the study, participants reported their state positive and negative affect and self-esteem. A secondary sample (N = 128) of adult participants rated the future event writing of the original participants. Supporting the validity of the study design, grandiose future events significantly differed from positive future events (e.g., they were rated by independent raters as less plausible, more ambitious, more active, and occurring further in the future). Participants scoring higher in narcissism and participants who experienced larger increases in negative affect were more likely to choose to engage in grandiose fantasizing. Grandiose fantasizing was more effective at decreasing negative affect among participants scoring higher in narcissism than those scoring lower in narcissism, whereas positive future thinking was equally effective at decreasing negative affect across levels of narcissism. This study demonstrates that people scoring higher in narcissism are more likely to choose to engage in grandiose fantasizing to make themselves feel better. It further demonstrates that grandiose fantasizing is a more efficacious affect regulator for those scoring higher in narcissism.

The contribution of affective content to cue-response correspondence in a word association task: Focus on emotion words and emotion-laden words

AbstractThis study aimed at examining the contribution of affective content to the organization of words in the lexicon. Based on existing free association norms and on a series of questionnaires we developed, we examined the characteristics of the words produced as associates to 840 Spanish cue words. Half of them were affective words and the other half were neutral (non-affective) words. Among the affective cue words, some words directly labeled an emotion (emotion words, EM) and others did not label an emotion but could elicit it (emotion-laden words, EL). The words produced as associates were also classified according to this distinction. Furthermore, we examined the relationship between the lexico-semantic and affective properties of the cue words and the associated words. The results revealed that EM, EL, and neutral associated words were elicited to a greater extent by cue words of the same type than by other types of cue words. Furthermore, the degree of correspondence between the affective properties of the cues and their associates was higher than that of lexico-semantic variables. These results have methodological implications for research on semantic memory and are of interest for applied studies focused on affective word organization in specific populations.

ANCW: Affective norms for 4030 Chinese words.

Affective information contained in words is gaining increased attention among neurolinguists and psycholinguists around the world. This study established the Affective Norms for Chinese Words (ANCW) with valence, arousal, dominance, and concreteness ratings for 4030 words that were Chinese adaptations of the CET-4 (The National College English Test Band 4) official syllabus. Despite the existing Chinese affective norms such as the Chinese Affective Words System (CAWS), the ANCW provides much more and richer Chinese vocabulary. By using 7-point (ranging from 1 to 7) Likert scales in a paper-and-pencil procedure, we obtained ratings for all variables from 3717 Chinese undergraduates. The ANCW norms possessed good response reliability and were compatible with prior normative studies in Chinese. The pairwise correlation analysis revealed quadratic relations between valence and arousal, arousal and dominance, as well as valence and concreteness. Additionally, valence and dominance, as well as arousal and concreteness, presented a linear correlation, and concreteness and dominance were correlated. The ANCW provides reliable and standardized stimulus materials for further research involving emotional language processing.

Do gender differences exist in letters of recommendation for reproductive endocrinology and infertility fellowship?

Objective(s)To determine if gender differences exist in letters of recommendation for Reproductive Endocrinology and Infertility fellowship. DesignCohort study SettingAcademic medical center SubjectsCohort of applicants to a reproductive endocrinology and infertility fellowship program at single Midwestern academic institution in a single year. Intervention(s)None Main Outcome MeasureQuantitative measures included linguistics inquiry and word count analysis, with four summary variables and twenty-five word categories, to quantify tone and themes present by gender. Performing qualitative analysis in parallel to linguistic analysis allowed for exploration of themes not conveyed in quantitative methods alone. Qualitative measures included frequency of code themes in letters of recommendation by applicant gender. Result(s)There were 272 letters from 72 applicants, 54 (76%) of which were women and 17 (24%) were men. One applicant was excluded because gender information was not specified; 269 letters were included in the quantitative linguistics and qualitative coding analysis. One hundred ten letters (41%) were written by women, and 159 (59%) by men. Letters of recommendation written for men had higher mean word count than those written for women (537 vs 474, p=0.04).Linguistics Inquiry and Word Count analysis exhibited more risk words used to describe men applicants (p=0.01). When comparing word categories by letter writer gender, women letter writers more frequently used communal, affect, and home word categories while men writers more frequently used affiliation-related words.Qualitative analysis revealed that letters for men applicants described leadership, altruism, rapport with patients, and strong endorsements more frequently, while women applicants’ letters more often mentioned doubt raisers and disclosures of personal life. Additionally, letters for women applicants more often described candidates as drama-free, not easy to “fluster,” and not complainers. Further, letters for women more frequently disclosed parenthood status. Conclusion(s)Gender differences exist among both letter writers and applicants. Qualitative analysis revealed that women applicants were more likely to be described with doubt raisers and personal life disclosures, while men were more likely to be described by their altruism and with a strong endorsement. Increasing bias awareness and implementing mitigation strategies would improve gender equity in letters of recommendation.