Emotional Tone Research Articles

Developing A Conceptual Framework for Sustainable Development Education Through Digital Tools: Qualitative Insights from Southwest China

This research delves into the intricate utilization of digital technology in higher education, aiming to construct a nuanced conceptual framework to interpret the digitalization of teaching sustainable development. A qualitative research design was employed, utilizing semi-structured in-depth interviews with 25 higher education teachers representing diverse experiences, backgrounds, and expertise from southwest China. The study also incorporated word frequency analysis and sentiment analysis to provide a comprehensive understanding of the emphasized themes and emotional tones in the discussions. NVivo 12.0 software facilitated meticulous coding and analytical abstraction, allowing the identification of core concepts and relationships among them. The results unveiled prominent themes such as Institutional Support and Policy Integration, Faculty Competence and Training, and Integration of Sustainable Development in Curricula. The sentiment analysis depicted a predominantly positive outlook on the integration of sustainability and digital tools in education, highlighting the perceived advancements and benefits in the field. The word frequency analysis further reinforced the focus on education, sustainability, and digital tools within the research discourse. The study contributes significantly to the academic discourse on digital sustainability education, providing intricate insights and a conceptual framework that can guide future research and practice in the field. It paves the way for refined educational methodologies, curriculum designs, and institutional policies, emphasizing the critical role of digital technology in education for sustainable development.

Implications of language use in posttraumatic nightmares on psychological symptoms.

The current study sought to understand how language use in posttrauma nightmare (PTNM) reports may be related to psychological symptoms over the course of treatment. Multiple regression analyses were conducted to examine the relationship between language use in PTNMs and psychological symptoms. Specifically, cognitive processing words (CPW) and emotional tone (ET) measured in PTNMs were analyzed in their relationship to posttraumatic stress disorder (PTSD) and depression symptom severity, as well as nightmare frequency and distress measured at the midtreatment and last treatment sessions. Follow-up hierarchical regressions were used to assess the relationship of language to nightmare severity when controlling for both PTSD and depression severity. The sample consisted of treatment-seeking predominately Caucasian females from the community with a history of criterion A trauma and weekly nightmares. CPW in the nightmare were negatively associated with PTSD and depression symptom severity, as well as nightmare frequency, at the time of the exposure session. Decreased CPW and negative ET within the nightmare remained significantly associated with nightmare frequency at the time of the exposure session, when controlling for PTSD and depression symptom severity. CPW in the rescripted PTNM were negatively associated with PTSD and depression symptom severity at the time of the last treatment session. These results suggest that language use in nightmares may reveal important information about underlying cognitive and emotional processes that may help understand the etiology and maintenance of PTSD symptoms, as well as support PTNMs as co-occurring symptoms requiring targeted treatment, and not merely secondary symptoms of PTSD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Coenesthesia or the Immediate Feeling of Existence: Maine de Biran and the Problem of the Unconscious between Physiology and Philosophy

Abstract The term “coenesthesia” was introduced at the end of the eighteenth century by the German physiologist Johann Christian Reil to designate the general perception of the living body through the nerves. Over the course of the nineteenth century, this notion circulated widely not only in Germany, but also in France, where it was developed in particular by Théodule Ribot. However, a good sixty years before Ribot, Maine de Biran had already employed the notion of “coenesthesia” to indicate the “immediate feeling of existence,” which he distinguished from the apperception of the self in relation to the body. What Biran emphasized, in his philosophical appropriation of “coenesthesia,” is the impersonal and mostly unconscious nature of this feeling, which never leaves us and plays a key role in the shaping of our personal and conscious life, unnoticeably influencing the course of our ideas, our character, and the emotional tone of our perception of the world. The purpose of this article is to study Biran’s philosophical use of the notion of “coenesthesia” in the last phase of his philosophical reflection (1823–1824) in relation to the emergence of the problem of the unconscious.

Stock Price Prediction Using Sentiment Analysis

Abstract: This research discusses the ever-changing nature of the stock market by using models such as LSTM and ARIMA to forecast stock prices. It recognizes the volatility and unpredictability of financial markets recognizing the need for robust tools that can accurately predict. Sentiment analysis is used for enhancing the accuracy of stock price prediction, drawing information from various sources like news resources and social media platforms that reflect public sentiments and opinions. We aim to analyze a thorough aspect of factors influencing stock prices by combining these sentiments with the models. The LSTM model is used to study long-term dependencies in stock price trends and ARIMA provides information into the time series components. Combining sentiment analysis helps us to scan the emotional tone around a particular stock and contributes valuable data to the prediction process. The integration between the machine learning models and sentiment analysis offers an extensive approach to predicting stock prices considering both past trends and current public sentiment. This study adds to the work in progress to develop more accurate and adaptive tools for exploring the difficulties of the stock market.

Graphanalytical Models of Actor Interaction

The article summarized and upgraded the research material dealing with agent modeling of entity behavior in active (man to man) viable systems. Aspects of emotional and behavior counterpart responses were studied in modeled social and economic processes of production contract implementation. The authors took into account the impact of dominating psycho-sociological styles of actor behavior included in the hierarchy of production interaction in case of asymmetric information and schemes of classical opportunism. The essence and location of psychological contracts in the process of concluding and implementing labour contracts were specified. 3D models of ‘principal – agent’ and ‘principal-manager-agent’ interactions were built with due regard to emotional responses and motivating decisions of process participants. The authors present the model, which can interpret actor interactions in the form of replacing each other chains of emotions and steps, which in case of graphic presentation of the process will be located in sequence on both sides of the Mebius loop analogues. The benefits of using KL-models and KM- and OK-functions in modeling emotional actor reaction in response to counterpart behavior were demonstrated. To provide better clearness necessary illustrations, schemes and graphs of two- and three-actor interactions were supplied. The provided schemes of iteractive processes of dynamic actor interactions allowed the authors to develop and effectively use software of modeling the emotional tone of hierarchical relations of entities in researching models of viable organization functioning. The developed mathematic tools got a certificate of state registration of computer databases and programs.

How Do Care Partners of People with Rare Dementia Use Language in Online Peer Support Groups? A Quantitative Text Analysis Study

We used quantitative text analysis to examine conversations in a series of online support groups attended by care partners of people living with rare dementias (PLWRD). We used transcripts of 14 sessions (>100,000 words) to explore patterns of communication in trained facilitators’ (n = 2) and participants’ (n = 11) speech and to investigate the impact of session agenda on language use. We investigated the features of their communication via Poisson regression and a clustering algorithm. We also compared their speech with a natural speech corpus. We found that differences to natural speech emerged, notably in emotional tone (d = −3.2, p < 0.001) and cognitive processes (d = 2.8, p < 0.001). We observed further differences between facilitators and participants and between sessions based on agenda. The clustering algorithm categorised participants’ contributions into three groups: sharing experience, self-reflection, and group processes. We discuss the findings in the context of Social Comparison Theory. We argue that dedicated online spaces have a positive impact on care partners in combatting isolation and stress via affiliation with peers. We then discuss the linguistic mechanisms by which social support was experienced in the group. The present paper has implications for any services seeking insight into how peer support is designed, delivered, and experienced by participants.

Student Feedback Mining System Using Sentiment Analysis

In education, the importance of gathering and understanding student feedback cannot be over stated. Traditional methods of gathering feedback through surveys and assessments often suffer from limitations such as low response rates and subjectivity. This paper proposes a new approach to improve the feedback collection process by implementing a Student Feedback Mining System using sentiment analysis. The goal of this system is to automatically analyze and extract sentiment from student feedback and provide educators and institutions with valuable insights into students' overall satisfaction and feelings toward various aspects of their academic experience. The system uses natural language processing (NLP) techniques to process unstructured text data, identify sentiments, and categorize feedback into positive, negative, or neutral feelings. Collecting student feedback through various channels such as online surveys, discussion forums and other feedback mechanisms. Cleaning and preprocessing the collected text data to remove noise, irrelevant information and standardize the format for analysis. Using state of the art NLP algorithms and sentiment analysis techniques to determine the emotional tone of feedback. This includes classifying sentiment as positive, negative, or neutral. Grouping feedback based on specific criteria such as course content, teaching methods, facilities and overall learning experience to provide a comprehensive understanding of the various aspects affecting student satisfaction. Presenting analyzed data through intuitive visualizations and reports, making it easy for educators and administrators to interpret and respond to feedback. Creating feedback that enables continuous improvement by providing actionable insights for educators to improve their teaching methods, curriculum and overall learning environment. The proposed system aims to overcome the limitations of traditional feedback collection methods by automating the analysis process, providing real-time insights and supporting a data-driven approach to improving education. Using sentiment analysis, educators and institutions can proactively solve problems, improve the quality of education, and create a more positive and engaging learning environment.

Viewer Engagement in Response to Mixed and Uniform Emotional Content in Marketing Videos-An Electroencephalographic Study.

This study presents the results of an experiment designed to investigate whether marketing videos containing mixed emotional content can sustain consumers interest longer compared to videos conveying a consistent emotional message. During the experiment, thirteen participants, wearing EEG (electroencephalographic) caps, were exposed to eight marketing videos with diverse emotional tones. Participant engagement was measured with an engagement index, a metric derived from the power of brain activity recorded over the frontal and parietal cortex and computed within three distinct frequency bands: theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz). The outcomes indicated a statistically significant influence of emotional content type (mixed vs. consistent) on the duration of user engagement. Videos containing a mixed emotional message were notably more effective in sustaining user engagement, whereas the engagement level for videos with a consistent emotional message declined over time. The principal inference drawn from the study is that advertising materials conveying a consistent emotional message should be notably briefer than those featuring a mixed emotional message to achieve an equivalent level of message effectiveness, measured through engagement duration.

Better abstract or concrete, narrating or not: optimal strategies for the communication of innovation

PurposeThis paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.Design/methodology/approachBased on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.FindingsThe authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.Research limitations/implicationsThis paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.Originality/valueThis paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

Examining the Effects of Communication Features of Educational Robots on Students’ Cognitive Load, Attitudes, and Learning Performance

Educational robots represent a unique form of teacher presence. Exploring how the communication features of robot instructors affect student learning experience could contribute to the advancement of educational robots. This study examined the impact of speech rate, voice type, and emotional tone of robots on students’ cognitive load, attitudes toward robot-assisted teaching, and learning performance. We recruited 477 Chinese primary school students assigned to either the speech rate, voice type, or emotional tone experiment. The results indicate that speech rate significantly influenced students’ cognitive load, with the medium speed condition resulting in higher germane load compared to both fast and slow speed conditions. Moreover, students had a lower preference for adult male voices over adult female, boy, or girl voices. However, voice type did not significantly impact attitudes toward robot-assisted teaching or learning outcomes. Emotional tone did not affect students’ cognitive load, attitudes, or learning performance. These findings provide valuable insights for instructors and designers when configuring the communication features of educational robots in classroom environments. Additionally, students generally prioritized the intelligence of the robot over its communication features, and they did not perceive the teaching content as difficult in all experiments. This study has methodological and practical significance.

NATURAL LANGUAGE PROCESSING EMPLOYING SENTIMENT ANALYSIS ON THE PUBLIC VOICE OF FILIPINOS DURING CRISIS SITUATIONS

Aims: This Natural Language Processing study examined the sentiments of Filipinos in crisis situations using Twitter data. The goal was to identify the prevalent emotional tone, categorize sentiments, and explore variations across different types of crises. Study design: Qualitative Research using Natural Language Processing Place and Duration of Study: Philippines, 2019 - 2023 Methodology: There were 10,000 tweets that narrate the public voice of Filipinos during crisis situations that were analyzed using sentiment analysis. They were mined using Apify Easy Twitter Search Scraper. Results were analyzed using Orange Software and Plutchik’s Emotional Wheel via Box Plot. Results: Results show that the dominant public voice is neutral, this is followed by positive and negative tones. This means that Filipinos mainly share information, describe their situations, and seek clarifications during crises. It is further revealed that joy was the most dominant emotion, followed by sadness, trust, surprise, fear, disgust, anticipation, and anger. This means that even in crisis, Filipinos wish peoples safety, pray for the situation and give jokes to lighten the mood. The order of dominant emotions or tone for earthquake, inflation, and typhoon followed a pattern of neutral, positive and negative. The pandemic stood out, featuring a dominant neutral tone, followed by negative and then positive. Conclusion: These Findings Offer Valuable Insights for Refining Government Crisis Response Strategies. Additionally, This Underscores the Significance Of Recognizing And Understanding Emotions In Communication, Providing Valuable Guidance For Educators And Students. KEYWORDS: Education, applied linguistics, natural language processing, sentiment analysis,public voice, emotional tone, Philippines

Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages

Review comments from digital platform such as Facebook, Twitter and YouTube used for identification of emotional tones from text. Nowadays, reviews are posted in different languages such as English, French, Chinese, and Indian regional languages such as Tamil, Telegu, and Hindi. Identification of emotional tones from text written in Indian regional language is challenging. During the translation of the regional language to the English language for sentiment analysis, lexical and pragmatic ambiguity are the major problem. The above problem arises due to dialects in language such as regional, standard, and social dialects. In this paper, dialect-based ambiguity problems solve through proposed Hybrid optimized deep learning transformer Models like M-BERT, M-Roberta, and M-XLM-Roberta for Tamil language dialects recognise and classified. The proposed algorithms provide better sentimental analysis after Hybrid optimization due to adaptation mechanisms, dynamic changes in the parameters and strategies in fine-tuning the search. The proposed Hybrid optimized algorithms perform better than existing algorithms such as SVM, Naïve Bayes, and LSTM with an accuracy of 95%.

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.

The Poison Pen: Slavery, Poison, and Fear in the Antebellum Press

ABSTRACT When enslaved people were accused of poisoning enslavers, it was newsworthy throughout the antebellum United States. Part of the broad appeal of such articles was their malleability; as reports of supposed poison plots or arsenic in a planter’s coffee travelled from newspapers in slave states to the pages of Northern publications, the articles morphed in form, purpose, and emotional tone. Southern readers looked to stories about thwarted poison plots to assuage their fears of a similar fate and present a brave united front. Meanwhile, Northern newspapers invited a host of other emotions from their audience, from enjoyment and schadenfreude to sympathy and anger. This article on messaging about slavery, poison, and fear in the antebellum United States builds on my monograph, Mastering Emotions: Feelings, Power, and Slavery in the United States, to focus on the emotional politics of fear. Examining how news about enslaved poisoners circulated and shapeshifted sheds light on the relationship between the antebellum press and collective emotions, and on the role of fear in defenses and critiques of slavery. While the Southern press framed stories about enslaved poisoners to address and manage members of the slaveocracy’s fears, the abolitionist press worked to invoke and amplify that terror.

Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.

The Value and Use of Chinese Traditional Music in Dance Performance in the Internet Era

Abstract In this study, Gaussian mixture model preprocessing and histogram of orientation gradient (HOG) and histogram of optical flow direction (HOF) action feature extraction techniques are used to significantly optimize the accuracy of dance performance action recognition. In addition, the VNect pose estimation algorithm and dual-stream 3D-CNN-based skeleton action recognition are used to improve significantly the accuracy and efficiency of recognition. In terms of music feature recognition, this study utilizes short-time time domain and frequency domain analysis techniques and BP neural networks to effectively achieve accurate recognition and sentiment classification of music features. Analyzing a dataset containing 6000 training videos, 800 validation videos, and 1200 test videos, the study reveals that musical elements, melody, rhythm, emotional tone, and song style significantly impact the stage performance effect. The study not only highlights the importance of traditional Chinese music in dance performance to enhance the artistic effect and deepen the emotional expression, but also opens up new perspectives and methods for disseminating and developing traditional culture.

Apocalypse now or later? Nuclear war risk perceptions mirroring media coverage and emotional tone shifts in Italian news

Abstract This study delves into the progression of nuclear war risk perceptions during the initial 6 months of the Ukraine war. It particularly investigated the influence of Italian media coverage changes and the affective tone of war representation. Utilizing a mixed-methods approach, two separate yet interconnected studies were conducted. The first study employed web scraping and keyword selection techniques to assess emotional language and quantify war-related content in the headlines of Italian online newspapers from March to July 2022. Results demonstrated a linear decrease in war-related news and an emotional shift, with a significant decrease in fear and an increase in joy noted between March and May. The second study examined nuclear war risk perceptions at an individual level, surveying a panel of 397 Italians at three distinct points during the same time frame. The findings revealed a similarity between the media’s affective tone and individuals’ affective risk perceptions. Analytic risk perception, in contrast, showed a linear decrease that matched the decline in war-related news volume. The study found preexisting individual differences, among women and older participants, to be significant determinants in shaping risk perception evolution. These groups exhibited higher initial risk perceptions and more resistance to change as the scenario unfolded. This research contributes to the existing body of work that underscores the media’s role in influencing risk perceptions by illuminating the relationship between media representation of the Ukraine war and individual-level affective risk perception. Furthermore, it highlights individual differences as significant moderators of risk perception change during a crisis.

Examining a sentiment algorithm on session patient records in an eating disorder treatment setting: a preliminary study.

Clinicians collect session therapy notes within patient session records. Session records contain valuable information about patients' treatment progress. Sentiment analysis is a tool to extract emotional tones and states from text input and could be used to evaluate patients' sentiment during treatment over time. This preliminary study aims to investigate the validity of automated sentiment analysis on session patient records within an eating disorder (ED) treatment context against the performance of human raters. A total of 460 patient session records from eight participants diagnosed with an ED were evaluated on their overall sentiment by an automated sentiment analysis and two human raters separately. The inter-rater agreement (IRR) between the automated analysis and human raters and IRR among the human raters was analyzed by calculating the intra-class correlation (ICC) under a continuous interpretation and weighted Cohen's kappa under a categorical interpretation. Furthermore, differences regarding positive and negative matches between the human raters and the automated analysis were examined in closer detail. The ICC showed a moderate automated-human agreement (ICC = 0.55), and the weighted Cohen's kappa showed a fair automated-human (k = 0.29) and substantial human-human agreement (k = 0.68) for the evaluation of overall sentiment. Furthermore, the automated analysis lacked words specific to an ED context. The automated sentiment analysis performed worse in discerning sentiment from session patient records compared to human raters and cannot be used within practice in its current state if the benchmark is considered adequate enough. Nevertheless, the automated sentiment analysis does show potential in extracting sentiment from session records. The automated analysis should be further developed by including context-specific ED words, and a more solid benchmark, such as patients' own mood, should be established to compare the performance of the automated analysis to.

Enhancing Sentiment Analysis Accuracy by Optimizing Hyperparameters of SVM and Logistic Regression Models

The Analysis of Sentiments expressed on Twitter is a widely practiced application of Natural Language Processing (NLP) and Artificial Intelligence (AI). This process involves examining tweets to determine the emotional tone conveyed within the message. AI-based approaches are employed in Twitter sentiment analysis, typically following these steps: Data Collection, Data Preprocessing, and Sentiment Analysis, where AI techniques like Support Vector Machines (SVM) and Logistic Regression are utilized to categorize tweets into positive, negative, or neutral sentiments. Twitter data is a valuable source of information, serving diverse purposes such as real-time updates, user feedback, brand monitoring, market research, digital marketing, and political analysis. The Twitter API (Application Programming Interface) provides developers with tools and functionalities to access and interact with Twitter data, including tweets, user profiles, and timelines, enabling a wide range of applications and services. However, Twitter sentiment analysis presents challenges such as handling sarcasm, irony, colloquial language, and coping with the sheer volume and rapid flow of Twitter data. Nevertheless, with effective preprocessing techniques and AI methods, Twitter sentiment analysis can yield valuable insights into public opinion on various topics.

Հուզական խոսքային ակտերի ուսուցումը հուզագնահատողական արտահայտությունների միջոցով / Teaching Expressive Speech Acts Through Emotional-Attitudinal Expressions

The present article explores the integration of expressive speech acts in the communicative learning process of a foreign language, primarily English. Speech acts play a significant pragmatic role in communication, and understanding how to perform them appropriately according to the context is essential. However, many general education textbooks for foreign languages lack activities that help learners perceive and use speech acts effectively, which hinders the development of their communicative skills. The paper specifically focuses on expressive speech acts, which bridge the speaker's emotions and the communicative intent. We view emotional-attitudinal expressions as distinct linguistic units for teaching expressive speech acts, for they serve as a tool for infusing an emotional tone into speech and elucidating the speaker's thoughts and feelings. To address this, we provide a brief analysis of communicative language competence and its components, discuss various types of speech acts, and highlight the importance of emotional-attitudinal expressions in both communication and foreign language learning. The article concludes with a set of exercises designed to enhance learners' understanding of speech acts and emotional-attitudinal expressions, guiding them towards effective use in foreign language communication.