Negative Emotion Words Research Articles

Interpersonal Goals and Language Use During the Disclosure of a Concealable Stigmatized Identity: A LIWC Analysis

Previous theorizing suggests that the first stage of self-disclosure is the pursuit of an interpersonal goal. However, among individuals disclosing a concealable stigmatized identity, little is known about how their goals impact downstream processes such as language use and post-disclosure affect. Using Linguistic Inquiry and Word Count (LIWC), we examined the extent to which primed self-image or compassionate goals influence the linguistic content and style of disclosure, as well as dyad members’ affective states. Self-image disclosures were high in negative self-focused attention, whereas compassionate disclosures were more inclusive and positive in valence. Moreover, among disclosers, negative emotion words predicted their own negative affect. Among confidants, first-person singular and second-person pronouns predicted negative affect, however, this outcome was inversely related to disclosers’ use of social words. Compassionate goals that seek mutually beneficial outcomes may help disclosers communicate information about their stigma more effectively, and attenuate dyad members’ feelings of emotional distress.

The construction of a Chinese fine-grained sentiment dictionary for Chinese domestic investors (CN-FSD) and its application

Stock bar comment texts are used to forecast stock market trends because they contain sentiment data provided by investors. However, previous research has relied on generic or polar sentiment dictionaries to construct sentiment indicators, which has limited progress in this field. This paper crawls nearly 1.8 million stock bar comment texts from the Eastmoney (www.eastmoney.com) Stock Bar Forum to perform sentiment analysis in this specialized area of the financial market. It reclassifies existing Chinese sentiment dictionaries, and utilizes Word2Vec and CNN models and word frequency statistics with a penalty mechanism to extract polar sentiment words. Additionally, the improved PMI (Pointwise Mutual Information) algorithm was utilized for fine-grained segmentation of negative emotion words. Finally, a fine-grained sentiment dictionary (four categories: happy, anger, sad and fear; with a total of 6155 words) was established for Chinese domestic investors (denoted by CN-FSD). We found that the improved PMI algorithm increased accuracy by 14%. In addition, we introduce the investor sentiment quantification indicators from two aspects, i.e., fine-grained sentiments and on the emotion theory dimension of psychology from the valence and arousal dimensions. This is done to verify the validity and robustness of the constructed dictionary. The case study from 2019 to 2022 discovered that under the influence of extreme occurrences like COVID-19, the fine-grained sentiment indicators constructed in this study are found to be significantly more strongly correlated with market trend indicators like index return and trading volume than the existing polar or fine-grained sentiment dictionaries. ‘fear’ emotions in fine-grained emotions dominate the stock market. Just as this case study emphasize the importance of ‘fear’ sentiment, studying the indicators of high arousal sentiment is also important. This case study further validates the efficacy of CN-FSD.

Expectation to rewards modulates learning emotional words: Evidence from a hierarchical Bayesian model.

In language acquisition, individuals learn the emotional value of words through external feedback. Previous studies have used emotional words as experimental materials to explore the cognitive mechanisms underlying emotional language processing, but have failed to recognize that languages are acquired in changing environments. To this end, this study aims to combine reinforcement learning with emotional word learning, using a probabilistic reversal learning task to explore how individuals acquire the valence of emotional words in a dynamically changing environment. Computational modeling on both behavioral and event-related potential (ERP) data revealed that individuals' expectations to rewards modulated the learning speed and temporal processing of emotional words, demonstrating a clear negative bias. Specifically, as the expected value increased, individuals responded faster and exhibited higher amplitudes for negative emotional words. These findings shed light on the neural mechanisms of emotional word learning in a volatile environment, highlighting the crucial role of expectations in this process and a preference for learning negative information.

Abstract B049: Building rapport in navigator-delivered health insurance literacy interventions: Engagement and tone used by parents of children with cancer and patient navigators

Abstract Purpose: To explore the linguistic elements of engagement and tone used by parents of children with cancer and a patient navigator (PN) during a pilot study of a health insurance literacy (HIL) intervention. Methods: A trained PN delivered a four-session Zoom-based HIL intervention for parents of children with cancer that contained content on: Session 1 (S1) health insurance terms, (S2) using health insurance, (S3) insurance appeals/legislation, and (S4) budgeting and financial strategies. We applied Linguistic Inquiry Word Count (LIWC-22) software to analyze transcriptions of each session regarding engagement and tone. We report, in aggregate, differences in language: analytical language (e.g., formal, logical), clout language (e.g., confidence, leadership), authenticity language (e.g., degree of self-monitoring), and emotional tone (e.g., positive and negative emotion words) (all range 1-99) between S1 and S4 using paired t-tests. We also analyzed language similarities between the PN and the parent using Language Style Matching, which represents the degree to which both speakers are engaged in the conversation. Results: Of 10 participants, 9 completed 4 sessions, and 1 parent completed 3 (39 sessions total); sessions were on average 35.1 (SD=6.4) minutes. Most participants were women (80%), married (90%), white (100%), and non-Hispanic (90%); half had a college degree. The PN spoke more than parents across all four sessions (90% PN vs 8.8% parent); however, parent engagement, as represented by word count, increased as the sessions went on (mean S1: 414 words, S4: 603.8 words, p=0.21). S1 contained the most language exchange between parents and the PN (median word count: 5,533.5). The discussions became increasingly authentic (mean by session: S1:24.2; S4:28.6; p=0.02), held more clout (S1:77.8, S4: 85.8; p=0.009), and increased in emotional tone (S1:49; S4:65, p=0.002). Analytical language decreased over time (S1:24.4; S4:19.8, p=0.05). Language style matching was consistently high and trending upward across sessions (S1:0.80; S4: 0.81, p=0.3). Conclusions: In a pilot test of a HIL intervention, we saw an increase in authentic language, emotional tone, and parent word count coupled with a decrease in analytical language, indicating increased rapport between the PN and parent over the sessions. This suggests there is emotional and supportive value in PN-led interventions as compared to self-directed interventions. PNs can build trust and promote equity by deconstructing and personalizing health insurance information. Future work should explore how rapport built between parents and PNs relates to health insurance literacy and financial toxicity outcomes. Citation Format: Mary K. Killela, Anne C. Kirchhoff, Perla Vaca Lopez, Amy Chevrier, Monique Stefanou, Katie Rogers, Douglas Fair, Echo L. Warner. Building rapport in navigator-delivered health insurance literacy interventions: Engagement and tone used by parents of children with cancer and patient navigators [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B049.

The development of an emotion lexicon for spicy food flavor experience

The sensory properties of food are the most important source of food-induced emotional responses. Spicy flavor is characterized by burning, stinging, or tingling, which gives a unique flavor to the sensory experience of food. The aim of the current study was to develop an emotion lexicon to measure the emotional responses to spicy food flavor. In Experiment 1, a focus group of frequent (N = 30) and infrequent (N = 30) spicy food consumers were interviewed to generate emotion words after tasting spicy food samples. After semantic analysis, comparison with published emotion lists, and positive and negative evaluations, a lexicon of 33 emotion words (21 positive and 12 negative emotion words) was developed for spicy flavor experiences. In Experiment 2, by applying the rate-all-that-apply technique, a separate group of participants (N = 91) rated the intensity of emotions before and after eating various spicy foods in small portions using the emotion lexicon generated in Experiment 1. Validation of the emotion lexicon shows that the high and the low spicy preference groups can be distinguished by most positive words (19 out of 21) and 4 negative words. Furthermore, participants’ spicy eating habits were significantly predicted by the positive or negative emotions associated with the spicy flavor experience during food tasting. Overall, the emotion lexicon serves as a valuable tool for investigating the affective correlates of spicy flavor across diverse spicy preference groups and holds promise for broader application to various products.

EVALUATION OF INDONESIAN VERSION OF THE EMOTION WORD FLUENCY TEST

A cognitive assessment is not complete without an inspection of a client’s emotional status. The Emotion Word Fluency Test (EWFT) is a newly developed instrument for Indonesia. It provides similar assessment to other semantic word fluency tasks, linguistic and memory skills, and also a client’s current emotional standing and previously experienced emotions. Our study aims to present the newly-developed EWFT and its scoring system, and the psychometric properties of the test including its test-retest reliability and construct validity, as well as the reliability of the scoring system. Healthy participants with no history of brain-related diseases (n = 98), varying in age and education, were recruited; an additional group (n = 30) was included in the test-retest reliability study. The construct validity was evaluated with a phonemic verbal fluency test (pVFT). The EWFT was correlated (.51-.56) with the pVFT, and the three subscales of the pVFT correlated highly (.75-.80), indicating that the EWFT and pVFT share a common construct, but are also different. The scoring system showed excellent reliability (.86 for the number of correct generated words), and the test-retest reliability for the number of correct words was moderate (.54). Participants produced more negative than positive emotion words, t(29) = 6.33, p < .05. The correlation between the number of positive and negative emotion words was zero. Age affected the performance in both EWFT, F(3, 80) = 3.10, p < .05; and pVFT F(3, 80) = 2.47, p < .05; as was internationally reported. The EWFT scoring system is concluded reliable and the test has moderate reliability.

The effects of different types of emotion words on emotion picture processing - evidence from event-related potential.

Emotional information can be seen everywhere in daily life. Research on emotional words often employs lexical decision tasks to judge the veracity of words, involving only superficial processing and not the deep processing of emotional significance. Therefore, the purpose of this study is to explore the effect of types of emotional words on the processing of emotional pictures. Participants were publicly recruited for a button-press experiment to discuss the impact of emotional words on the processing of emotional pictures from both behavioral and physiological mechanisms. The results of experiment 1 show: (a) in terms of reaction time, the processing speed for negative emotional words was slower, with longer reaction times; (b) In terms of accuracy, positive emotional words had a higher correct rate than negative emotional words. The results of experiment 2 found: (a) a significant main effect of emotional word type in the late processing stage; (b) a significant interaction between emotional word type and congruency. Previously presented emotional words affect the processing of subsequently presented emotional pictures, with differences in the processing of the four types of words, indicating a significant role of language in emotional processing.

Linguistic style as a digital marker for depression severity: An ambulatory assessment pilot study in patients with depressive disorder undergoing sleep deprivation therapy.

Digital phenotyping and monitoring tools are the most promising approaches to automatically detect upcoming depressive episodes. Especially, linguistic style has been seen as a potential behavioral marker of depression, as cross-sectional studies showed, for example, less frequent use of positive emotion words, intensified use of negative emotion words, and more self-references in patients with depression compared to healthy controls. However, longitudinal studies are sparse and therefore it remains unclear whether within-person fluctuations in depression severity are associated with individuals' linguistic style. To capture affective states and concomitant speech samples longitudinally, we used an ambulatory assessment approach sampling multiple times a day via smartphones in patients diagnosed with depressive disorder undergoing sleep deprivation therapy. This intervention promises a rapid change of affective symptoms within a short period of time, assuring sufficient variability in depressive symptoms. We extracted word categories from the transcribed speech samples using the Linguistic Inquiry and Word Count. Our analyses revealed that more pleasant affective momentary states (lower reported depression severity, lower negative affective state, higher positive affective state, (positive) valence, energetic arousal and calmness) are mirrored in the use of less negative emotion words and more positive emotion words. We conclude that a patient's linguistic style, especially the use of positive and negative emotion words, is associated with self-reported affective states and thus is a promising feature for speech-based automated monitoring and prediction of upcoming episodes, ultimately leading to better patient care.

Neural correlates of depression-related smartphone language use in adolescents

Magnetic resonance imaging has provided pathophysiological insights into adolescent depression but is a relatively inaccessible technology. Generating scalable indicators of depression that are informed by neuroscience is therefore critical for providing solutions that allow us to detect and treat this devastating disorder. In this preregistered study, we investigated whether passively acquired smartphone-based language usage represents such an indicator of depression and explored whether the neural correlates of depression mediate or moderate this association. Forty adolescents (ages 14–18 years) with (n = 26) and without (n = 14) depression completed clinical assessments and a resting-state fMRI scan, prior to downloading a passive mobile sensing app to their smartphones. Linguistic features derived from over 1.2 million words (319,364 messages) across all smartphone apps were used to examine word usage patterns. Independent components analysis followed by dual regression was used to derive intrinsic networks commonly associated with depression: central executive network (CEN), default mode network (DMN), and salience network (SN). Depression was associated with more negative emotion word usage and fewer future-focus word usage on a daily basis (all ps < 0.05). Higher depressive symptoms and brain networks DMN and CEN were associated with greater first-person pronoun usage (all ps < 0.04). Accounting for CEN connectivity amplified the positive association between depressive symptoms and first-person pronoun usage. Lower SN–CEN connectivity moderated the association between depression and negative emotion word usage. Depression in adolescents is associated with naturalistic language usage during smartphone activities and may represent neurocognitive biases that are candidate treatment targets for interventions.

Exploring barriers to care home research recruitment during the COVID-19 pandemic: The influence of social media recruitment posts and public sentiment.

Recruitment of care home staff to research studies is recognised as challenging. This was further exacerbated by the COVID-19 pandemic and the associated negative media portrayal of care home workers. Social media use has surged since the onset of COVID-19 lockdowns, offering a plausible approach to understanding the barriers to care home research recruitment and gaining insight into public perceptions of care home workers. To utilise comments from two Facebook recruitment posts to: 1) gain an understanding of potential barriers to recruitment of healthcare workers (HCWs) in UK care homes, and 2) explore public sentiment towards care home research and care homes in the context of the COVID-19 pandemic. This cross-sectional study analysed comments from two Facebook posts (available June-October 2021) advertising a separate study on psychological support for care staff during the pandemic. This study was situated within a larger investigation into the mental health and wellbeing of care home staff and employed both qualitative analysis and quantitative methods (word count and correlations between words used and between posts). Three themes were identified from the qualitative analysis: support, mistrust and blame. There was a greater use of words associated with support and negative emotive words in post 2. Post 2 comments featured significantly more choice words and first-person singular pronouns than post 1 which indicated a resentful sentiment from those who advocate freedom of choice and control. Discussion of mistrust towards researchers was most prominent in post 1 indicating the importance of relationship building between researchers and HCWs in UK care homes. With attribution to blame, there was a larger range of negative emotion words than positive emotion words. Taken together our findings offer novel insights into why recruitment to care home research during the pandemic including the use of social media might be problematic. Social media is a useful tool for recruitment but should not be considered as a one-time input. Researchers should pro-actively engage with the study population from the start using co-design with resident and public groups to support recruitment and ensure these populations are accurately represented within research.

Effect of contextual prediction on emotional word processing: an evidence from ERPNR-D-24-00189.

This study examined the effect of context on the prediction of emotional words with varying valences. It investigated the neural mechanisms underlying the processing differences of emotion words with different valences in both predictable and unpredictable contexts. Additionally, it aimed to address the conflicting results regarding the processing time in predictive contexts reported in previous studies. Participants were instructed to carefully read the text that included the specified emotion words. Event-related potentials elicited by emotional words were measured. To ensure that the participants can read the text carefully, 33% of the texts are followed by comprehension problems. After reading the text, the comprehension questions were answered based on the text content. The study revealed that the N400 amplitude elicited by an unpredictable context was greater than that elicited by a predictable context. Additionally, the N400 amplitude triggered by positive emotion words was larger than that triggered by negative emotion words. However, there was no significant difference in late positive component amplitude observed between contextual prediction and emotional word valence. The present study suggests that predictive processing takes place at an intermediate stage of speech processing, approximately 400 ms after stimulus onset. Furthermore, the presence of a predictive context enhances the processing of emotional information. Notably, brain activity is more pronounced during the processing of positive emotional stimuli compared to negative emotional stimuli. Additionally, the facilitative effect of a predictable context diminishes in the advanced phase of Chinese speech comprehension.

Which Linguistic Cues Make People Fall for Fake News? A Comparison of Cognitive and Affective Processing

Fake news on social media has large, negative implications for society. However, little is known about what linguistic cues make people fall for fake news and, hence, how to design effective countermeasures for social media. In this study, we seek to understand which linguistic cues make people fall for fake news. Linguistic cues (e.g., adverbs, personal pronouns, positive emotion words, negative emotion words) are important characteristics of any text and also affect how people process real vs. fake news. Specifically, we compare the role of linguistic cues across both cognitive processing (related to careful thinking) and affective processing (related to unconscious automatic evaluations). To this end, we performed a within-subject experiment where we collected neurophysiological measurements of 42 subjects while these read a sample of 40 real and fake news articles. During our experiment, we measured cognitive processing through eye fixations, and affective processing in situ through heart rate variability. We find that users engage more in cognitive processing for longer fake news articles, while affective processing is more pronounced for fake news written in analytic words. To the best of our knowledge, this is the first work studying the role of linguistic cues in fake news processing. Altogether, our findings have important implications for designing online platforms that encourage users to engage in careful thinking and thus prevent them from falling for fake news.

Feelings in words: Emotion word use and cardiovascular reactivity in marital interactions.

Putting feelings into words is often thought to be beneficial. Few studies, however, have examined associations between natural emotion word use and cardiovascular reactivity. This laboratory-based study examined emotion word use (i.e., from computerized text analysis) and cardiovascular reactivity (i.e., interbeat interval changes from baseline) across two interaction contexts (i.e., conflict and positive conversations) in 49 mixed-sex married couples (age: M = 43.11, SD = 9.20) from diverse socioeconomic backgrounds. We focused on both frequency (i.e., relative proportion of emotion words) and diversity (i.e., relative proportion of unique emotion words) of emotion words. Data were collected between 2015 and 2017 and analyzed treating both partners and conversations as repeated measures, resulting in 196 observations overall (four per dyad). Findings showed that (a) when spouses used more negative emotion words (especially anger), they showed higher cardiovascular reactivity. This finding was robust when controlling for covariates; generalized across gender, interaction contexts, and socioeconomic status. Moreover, (b) when spouses used a more diverse negative emotion vocabulary, they showed higher cardiovascular reactivity, but this was not robust when controlling for negative emotion word frequency. Associations between (c) positive emotion word use and cardiovascular reactivity were not statistically significant. Verbalizing negative emotions thus seems to go along with higher cardiovascular reactivity, at least in the short term. Replication is needed across other relationship types, genders, and sexual orientations. These findings highlight emotion word use as an indicator of cardiovascular reactivity, which has implications for the identification of potential health risks that emerge during marital interactions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Emotion language use in narratives of the 9/11 attacks predicts long-term memory.

Despite considerable cognitive neuroscience research demonstrating that emotions can influence the encoding and consolidation of memory, research has failed to demonstrate a relationship between self-reported ratings of emotions collected soon after a traumatic event and memory for the event over time. This secondary analysis of data from a multisite longitudinal study of memories of the September 11, 2001 terrorist attacks, asked the question of whether emotional language use could predict memory over time. In the 2 weeks following the 9/11 attacks, participants (N = 691; Mage = 36.8; 72% identifying as male; 76% identifying as white) wrote narratives about how they learned of the attacks and the impact of the attacks on them. Language features of these narratives were extracted using the Linguistic Inquiry Word Count program and used to predict three types of memory: (a) event memory accuracy, (b) flashbulb memory consistency, and (c) emotion memory consistency. These outcomes were assessed at the time of writing, 1, 3, and 10 years after the 9/11 attacks. Results of linear mixed-effects models indicate that greater use of negative emotion words in narratives predicts better event memory accuracy 3 and 10 years after the attacks and worse flashbulb memory consistency 10 years after the attacks. However, emotion word use does not predict emotion memory consistency across time. We also examine whether other exploratory linguistic predictors are associated with memory over time. These findings suggest that written language may serve as a potential early indicator of memory over time. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A tale of two platforms: A comparative analysis of language use in consumer complaints on Reddit and Spotify Community

AbstractConsumer complaints play a crucial role for companies in understanding customer needs and spotting possible areas of improvement. In the digital era, online platforms have become popular venues for users to voice their concerns. According to Communication Accommodation Theory (CAT) and recipient design, consumers may have different recipients in mind when writing a complaint and tailor their language accordingly. This study aims to examine language variations in consumer complaints on an independent platform and a corporate platform, exploring how consumers adjust their language based on the intended recipients of their complaints. Comments under complaint posts from Reddit's dedicated subreddit, r/Spotify, and from the Spotify Community were extracted and analyzed using the Linguistic Inquiry and Word Count (LIWC), a text analysis tool. This comparative analysis identified and compared various linguistic categories within the consumer complaints from the perspectives of formality, emotion, and assertion. The results showed that complaints on the corporate platform (1) used more formal language, (2) contained more negative emotional tones and emotion words, and (3) exhibited more assertiveness compared to those on the independent platform. Understanding the distinct linguistic features in consumer complaints on different platforms can help companies tailor their responses and address customer concerns effectively. This research contributes to the growing field of online consumer complaining behavior by uncovering how language is employed within different online communities depending on the message recipient. Furthermore, it demonstrates the potential of LIWC as a valuable tool for analyzing unstructured consumer‐generated content across diverse digital platforms, offering insights into user sentiment and experiences.

Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese

There is an emerging potential for digital assessment of depression. In this study, Chinese patients with major depressive disorder (MDD) and controls underwent a week of multimodal measurement including actigraphy and app-based measures (D-MOMO) to record rest-activity, facial expression, voice, and mood states. Seven machine-learning models (Random Forest [RF], Logistic regression [LR], Support vector machine [SVM], K-Nearest Neighbors [KNN], Decision tree [DT], Naive Bayes [NB], and Artificial Neural Networks [ANN]) with leave-one-out cross-validation were applied to detect lifetime diagnosis of MDD and non-remission status. Eighty MDD subjects and 76 age- and sex-matched controls completed the actigraphy, while 61 MDD subjects and 47 controls completed the app-based assessment. MDD subjects had lower mobile time (P = 0.006), later sleep midpoint (P = 0.047) and Acrophase (P = 0.024) than controls. For app measurement, MDD subjects had more frequent brow lowering (P = 0.023), less lip corner pulling (P = 0.007), higher pause variability (P = 0.046), more frequent self-reference (P = 0.024) and negative emotion words (P = 0.002), lower articulation rate (P < 0.001) and happiness level (P < 0.001) than controls. With the fusion of all digital modalities, the predictive performance (F1-score) of ANN for a lifetime diagnosis of MDD was 0.81 and 0.70 for non-remission status when combined with the HADS-D item score, respectively. Multimodal digital measurement is a feasible diagnostic tool for depression in Chinese. A combination of multimodal measurement and machine-learning approach has enhanced the performance of digital markers in phenotyping and diagnosis of MDD.

Diffusion of tax-related communication on social media

Taxation is a recurrent topic in people's conversations, also on social media. Yet, informal channels such as social media have been widely neglected in studies that examined how information about taxation spreads across social networks. Using posts on Twitter (currently called “X”) with taxation related hashtags from 2010 to 2020, we examined what linguistic cues are associated with information diffusion, that is, the number of retweets a message receives. The use of emotional, moral, and moral-emotional language in a tweet was associated with greater diffusion (i.e., more retweets). In contrast to the negativity bias literature, positive emotional words were more strongly associated with information diffusion than negative emotional words. Among the specific emotions that taxation research has focused on, only the use of anger (but not anxiety) words was associated with more retweets. The study contributes to the literature by examining individuals’ reasoning about taxes.

Exploring the role of executive functions in Chinese-English bilinguals’ emotional word comprehension during language switching

ABSTRACT Switching between languages requires the participation of executive functions (EFs) which comprise both the ‘hot’ affective aspects (hot EFs) and the ‘cool’ cognitive aspects (cool EFs). However, the role of these EFs in language switching is not clear. This study employed a word valence categorization task to explore the effects of hot (i.e. emotional valence and emotional congruency) and cool (i.e. inhibitory control ability, IC ability) EFs on high- and low-IC Chinese-English bilinguals’ language switching costs. The results showed smaller language switching costs for positive than negative emotional words, for emotional incongruent than congruent trials, for positive than negative emotional words in emotional congruent trials and for negative emotional words in emotional incongruent than congruent trials. Moreover, hot and cool EFs worked interactively, with larger language switching costs for negative emotional words and the same pattern in emotional congruent trials for high-IC participants over low-IC ones. These results indicate that various aspects of hot EFs affect language switching costs individually and interactively, while also modulate the effects of cool EF on language switching costs during Chinese-English emotional word comprehension. These findings have implications for extending the Bilingual Interactive Activation plus (BIA+) model by incorporating both hot and cool EFs.

The Emotional Content of Children's Writing: A Data-Driven Approach.

Emotion is closely associated with language, but we know very little about how children express emotion in their own writing. We used a large-scale, cross-sectional, and data-driven approach to investigate emotional expression via writing in children of different ages, and whether it varies for boys and girls. We first used a lexicon-based bag-of-words approach to identify emotional content in a large corpus of stories (N>100,000) written by 7- to 13-year-old children. Generalized Additive Models were then used to model changes in sentiment across age and gender. Two other machine learning approaches (BERT and TextBlob) validated and extended these analyses, converging on the finding that positive sentiments in children's writing decrease with age. These findings echo reports from previous studies showing a decrease in mood and an increased use of negative emotion words with age. We also found that stories by girls contained more positive sentiments than stories by boys. Our study shows the utility of large-scale data-driven approaches to reveal the content and nature of children's writing. Future experimental work should build on these observations to understand the likely complex relationships between written language and emotion, and how these change over development.

Speak English in the department or perish? A multimethod study on online public responses to an English-only incident at a US university

ABSTRACT This study investigated more than 3600 online comments responding to an incident in 2019 where a controversial memo was sent to instruct Chinese graduate students at a US university not to speak Chinese in the department. A text analysis based on Linguistic Inquiry and Word Count software showed that these comments included more negative-emotion words compared with positive-emotion words. In addition, an inductive thematic analysis identified themes supporting monolingual language ideology and other themes supporting language freedom in the United States and questioning the context of this English-only incident.