Emotion Classification Research Articles

Sentiment Analysis of Hotel Reviews in Anyer Beach Tourism Area: A Lexicon-Based Text Mining Approach

This study aims to analyze tourist sentiment towards hotels in the Anyer beach tourism area through online reviews. Sentiments and emotions expressed in these reviews were analyzed using a lexicon-based approach from the National Research Council (NRC) lexicon, which includes emotion categories such as anger, fear, sadness, joy, and disgust. Hotel review data were collected from TripAdvisor, and the analysis involved text preprocessing steps such as cleaning, case folding, stemming, and stopword removal. The sentiment analysis results reveal that most tourists provided positive reviews, with only 14.06% of the reviews displaying negative sentiment. Word cloud visualization identified the words "bad" and "dirty" as frequently appearing in negative reviews, likely pointing to cleanliness and service quality issues as the primary reasons for dissatisfaction. Emotional analysis showed dominant feelings of sadness, anger, disgust, and fear, likely triggered by unmet expectations regarding cleanliness and comfort during their stay. Based on these findings, it is recommended that hotel management in the Anyer beach tourism area address these concerns by implementing regular cleaning routines and staff training to enhance hotel service quality.

The aging of emotional words processing in implicit and explicit emotion task: an ERP study

Abstract This study examined how cognitive aging affects emotional word processing using event-related potential technique. Young and older adults completed both implicit lexical decision and explicit emotion categorization tasks involving positive, negative, and neutral words. Behaviorally, older adults displayed a negative emotion effect in the implicit task, which was absent in young adults. While both age groups exhibited both positive and negative emotion effects in the explicit task, older adults demonstrated a greater positivity bias compared to young adults. Event-related potential technique data revealed that young adults exhibited an early negative emotion effect on the P2 and a late emotion effect on the late positivity potentials in the implicit task. In contrast, older adults exhibited an early negativity bias effect on the P2, as well as both negative and positive emotion effects on the N400, and positive emotion effects on the late positivity potentials. In the explicit task, young adults showed both early and late negative emotion effects on the P2 and late positivity potentials, while older adults showed both negative and positive emotion effects on the late positivity potentials. The results suggest distinct processing mechanisms for emotion words in young and older adults, involving both bottom–up and top–down mechanisms, which support the socioemotional selectivity theory.

Category-Sensitive Age-Related Shifts Between Prosodic and Semantic Dominance in Emotion Perception Linked to Cognitive Capacities.

Prior research extensively documented challenges in recognizing verbal and nonverbal emotion among older individuals when compared with younger counterparts. However, the nature of these age-related changes remains unclear. The present study investigated how older and younger adults comprehend four basic emotions (i.e., anger, happiness, neutrality, and sadness) conveyed through verbal (semantic) and nonverbal (facial and prosodic) channels. A total of 73 older adults (43 women, Mage = 70.18 years) and 74 younger adults (37 women, Mage = 22.01 years) partook in a fixed-choice test for recognizing emotions presented visually via facial expressions or auditorily through prosody or semantics. The results confirmed age-related decline in recognizing emotions across all channels except for identifying happy facial expressions. Furthermore, the two age groups demonstrated both commonalities and disparities in their inclinations toward specific channels. While both groups displayed a shared dominance of visual facial cues over auditory emotional signals, older adults indicated a preference for semantics, whereas younger adults displayed a preference for prosody in auditory emotion perception. Notably, the dominance effects observed in older adults for visual and semantic cues were less pronounced for sadness and anger compared to other emotions. These challenges in emotion recognition and the shifts in channel preferences among older adults were correlated with their general cognitive capabilities. Together, the findings underscore that age-related obstacles in perceiving emotions and alterations in channel dominance, which vary by emotional category, are significantly intertwined with overall cognitive functioning. https://doi.org/10.23641/asha.27307251.

YOLO-Based Analysis of Pet Emotional Behavior to Emotion Classification in Dogs and Cats

YOLO-Based Analysis of Pet Emotional Behavior to Emotion Classification in Dogs and Cats

Emotion Recognition in Kurdish Speech from the Sorani Dialect Corpus

Given the increasing need for interactive human-computer applications, the field of employing machine learning algorithms to discern emotions from speech has seen a substantial surge in interest. While emotion recognition systems have made substantial progress in languages like German, English, Spanish, Dutch, and Danish, the availability of comprehensive datasets for the Kurdish language remains notably limited. This paper addresses this gap by focusing on emotion recognition in Sorani Kurdish dialect speech data, which was carefully gathered from openly available videos from the YouTube platform and categorized into four clear supposed emotions: neutral, sadness, happiness, and anger. The study applied both natural Mel Spectrogram and Mel-Frequency Cepstral Coefficient (MFCC) features for various spectrals, followed by the classification models K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) to evaluate the results. By closely examining and contrasting the results of using several methods for feature extraction, it was found that SVM obtained a higher accuracy, reaching as much as 85.57%. This is so much more than the first Kurdish emotion classification technique for the recognition of the emotion of the words.

Where are Emotions in Text?

Natural language processing (NLP) boasts a vibrant tradition of emotion studies, unified by the aim of developing systems that generate and recognize emotions in language. The computational approximation of these two capabilities, however, still faces fundamental challenges, as there is no consensus on how emotions should be processed, particularly in text: application-driven works often lose sight of foundational theories that describe how humans communicate what they feel, resulting in conflicting premises about the type of data best suited for modeling and whether this modeling should focus on textual meaning or style. My thesis fills in these theoretical gaps that hinder the creation of emotion-aware systems, demonstrating that a trans-disciplinary approach to emotions, which accounts for their extra-linguistic characteristics, has the potential to improve their computational processing. I investigate the human ability to detect emotions in written productions, and explore the linguistic dimensions that contribute to the emergence of emotions through text. In doing so, I clarify the possibilities and limits of automatic emotion classifiers and generators, also providing insights into where systems should model affective information.

Effect of observer's cultural background and masking condition of target face on facial expression recognition for machine-learning dataset.

Facial expression recognition (FER) is significantly influenced by the cultural background (CB) of observers and the masking conditions of the target face. This study aimed to clarify these factors' impact on FER, particularly in machine-learning datasets, increasingly used in human-computer interaction and automated systems. We conducted an FER experiment with East Asian participants and compared the results with the FERPlus dataset, evaluated by Western raters. Our novel analysis approach focused on variability between images and participants within a "majority" category and the eye-opening rate of target faces, providing a deeper understanding of FER processes. Notable findings were differences in "fear" perception between East Asians and Westerners, with East Asians more likely to interpret "fear" as "surprise." Masking conditions significantly affected emotion categorization, with "fear" perceived by East Asians for non-masked faces interpreted as "surprise" for masked faces. Then, the emotion labels were perceived as different emotions across categories in the masking condition, rather than simply lower recognition rates or confusion as in existing studies. Additionally, "sadness" perceived by Westerners was often interpreted as "disgust" by East Asians. These results suggest that one-to-one network learning models, commonly trained using majority labels, might overlook important minority response information, potentially leading to biases in automated FER systems. In conclusion, FER dataset characteristics differ depending on the target face's masking condition and the diversity among evaluation groups. This study highlights the need to consider these factors in machine-learning-based FER that relies on human-judged labels, to contribute to the development of more nuanced and fair automated FER systems. Our findings emphasize the novelty of our approach compared to existing studies and the importance of incorporating a broader range of human variability in FER research, setting the stage for future evaluations of machine learning classifiers on similar data.

Do physicians and patients speak different languages: quantitative evidence from linguistic analysis

Abstract Background Misunderstanding and miscommunication between physicians and patients are well-accepted sources of poor therapy adherence and poor outcomes. Some of these issues are suspected to occur because these two groups speak different "languages". A likely component of such language difference is vocabulary; specifically, the use of medical terminology. Nonetheless, there are other psychological aspects of language such as authoritative and emotional use. The text analysis programme, Linguistic Inquiry Word Count (LIWC), was used to assess such features. LIWC is an established tool that analyses the frequency of words that fall into various linguistic, psychological, and emotional categories. The aim was to compare text from both physicians and patients. The context was the use of anticoagulation therapy in patients with atrial fibrillation. Methods Three articles from English-speaking countries (Australia, Canada, and the UK) were examined. These articles presented transcribed verbal communications between interviewers and groups of physicians and patients. More than 80 separate comments, each ≥ 20 words, made by each group were extracted from the articles and analysed. The total number of words was 8,609 from physicians and 4,804 from patients. Data were collected for the following LIWC analysis categories: (1) the proportion of big words (words with ≥7 letters), (2) clout; high scores suggest that the speaker is confident, authoritative, and has a high level of social dominance. It implies that the individual is speaking from a position of strength or influence; and (3) emotion: this category is integral to understanding how people communicate feelings and emotions. Results As anticipated the number of big words was higher in physician interviews (19±1%) than for patients (12±1%; P=0.0013). The clout indicator also showed the physicians with a higher score (43±6 vs 16±6; P=0.028). In contrast, in the emotion category, patient scores (1.19±0.02) were higher than for physicians (0.58±0.15; P=0.016); primarily because of a three-fold higher negative emotion score (0.53±0.08 vs 0.16±0.11; P=0.048). Conclusions Physicians and patients used language differently. As expected, vocabulary differed; physicians used more big words. This is consistent with greater use of medical terminology, a known source of miscommunication. The higher physician clout score indicates the potential for a power imbalance. The greater use of emotion-related words by patients is consistent with their anxiety and concern. Human judgment and contextual analysis are required to fully understand and address the nuances of communication and associated understanding. Nevertheless, LIWC can play a supportive role. If we identify language patterns likely to result in miscommunication or misunderstanding, steps can be taken to mitigate the effects. LIWC provided quantitative insight into qualitative impressions and revealed subtle, but potentially vital, differences.

Performance Metrics for Multilabel Emotion Classification: Comparing Micro, Macro, and Weighted F1-Scores

This study compares various F1-score variants—micro, macro, and weighted—to assess their performance in evaluating text-based emotion classification. Lexicon distillation is employed using the multilabel emotion-annotated datasets XED and GoEmotions. The aim of this paper is to understand when each F1-score variant is better suited for evaluating text-based multilabel emotion classification. Unigram lexicons were derived from the annotated GoEmotions and XED datasets through a binary classification approach. The distilled lexicons were then applied to the GoEmotions and XED annotated datasets to calculate their emotional content, and the results were compared. The findings highlight the behavior of each F1-score variant under different class distributions, emphasizing the importance of appropriate metric selection for reliable model performance evaluation in imbalanced multilabel datasets. Additionally, this study also investigates the effect of the aggregation of negative emotions into broader categories on said F1 metrics. The contribution of this study is to provide insights into how different F1-score variants could improve the reliability of multilabel emotion classifier evaluation, particularly in the context of class imbalance present in the case of phishing emails.

Two languages, two emotional minds in one brain: processing emotion-label and emotion-laden words by Chinese-English bilinguals

ABSTRACT The present study examined the neural correlates of emotion effects evoked by emotion-label and emotion-laden nouns in Chinese-English bilinguals’ two languages through the emotion categorization tasks. At the perceptual processing stage, only L2 emotion-label and emotion-laden nouns induced amplified N100 than neutral nouns. At the semantic processing stage, relative to neutral nouns, L2 negative emotion-laden nouns triggered heightened EEPN, and L1 emotion-laden nouns evoked marginally smaller EEPN; only L2 emotion-laden and positive emotion-label nouns exhibited augmented LEPN; L2 positive emotion-label and L1 emotion-label and emotion-laden nouns displayed reduced N400. At the integration processing stage, L2 positive nouns showed larger LPC and L1 positive nouns displayed smaller LPC than neutral nouns. These results verified emotion word type hypothesis across languages, and also implied that emotional attributes were encoded within both perceptual and semantic representations in L2, and predominantly within semantic representation in L1.

FERDCNN: an efficient method for facial expression recognition through deep convolutional neural networks

Facial expression recognition (FER) has caught the research community’s attention recently because it can affect many real-life applications. Multiple studies have focused on automatic FER, most of which use a machine learning methodology, FER has continued to be a difficult and exciting issue in computer vision. Deep learning has recently drawn increased attention as a solution to several practical issues, including facial expression recognition. This article introduces an efficient method for FER (FERDCNN) verified on five different pre-trained deep CNN (DCNN) models (AlexNet, GoogleNet, ResNet-18, ResNet-50, and ResNet-101). In the proposed method, firstly the input image has been pre-processed using face detection, resizing, gamma correction, and histogram equalization techniques. Secondly, the images go through DCNN to extract deep features. Finally, support vector machine (SVM) and transfer learning are used to classify generated features. Recent methods have been employed to evaluate and contrast the performance of the proposed approach on two publicly standard databases namely, CK+ and JAFFE on the seven classes of fundamental emotions, including anger, disgust, fear, happiness, sadness, and surprise beside neutrality for CK+ and contempt for JAFFE. The suggested method tested Four different traditional supervised classifiers with deep features, Experimental found that AlexNet excels as a feature extractor, while SVM demonstrates superiority as a classifier because of this combination achieving the highest accuracy rates of 99.0% and 95.16% for the CK+ database and the JAFFE datasets, respectively.

Capturing multiple emotions from conversational data using fine tuned transformers

Emotion detection is one of the crucial topics of Natural Language Processing (NLP) in recent years, and now one of the biggest motivating factors in correct identification and interpretation of a wide range of emotional expressions in textual data. This research examines the use of Bidirectional Encoder Representations from Transformers (BERT), a trained transformer model for emotion detection in textual data. This analysis evaluates how good BERT is at identifying emotions such as surprise, anger, fear, happiness and sadness compared with ordinary machine learning as well as deep learning techniques. The addition of weighted emotions approach enhances the model performance and gives a deeper emotional context awareness making it more effective to deal with complicated emotional utterances. The purpose of the research is to develop a fine-tuned BERT model with a weighted emotion framework to enhance the accuracy of emotion classification in conversational text. The context is improving emotion recognition in scenarios where multiple emotions co-exist, addressing limitations in traditional models by capturing subtle and overlapping emotional expressions. In terms of training the methods multiple datasets are considered and also the research examines various models performance. The article further discusses possible application areas of BERT modified in light of NLP.

Category-dependent contribution of dog facial and bodily cues in human perception of dog emotions

The increasing popularity of human-dog interaction in our society calls for fuller understanding of humans’ ability to appraise dogs’ affective states, yet most research only focuses on recognizing dog facial expressions of primary/basic emotions. While the face is the dominant human emotional expression channel, bodily cues are also informative indicators of dog emotional states. In this online study, with dynamic and naturalistic videos depicting a common range of dog primary (anger, disgust, fear, happiness, sadness, surprise) and secondary emotions (appeasement, frustration, pain, positive anticipation, separation-distress), we compared human performance from 447 participants in categorizing dog facial expressions (with a visible dog face only) and bodily expressions (with a visible dog face and body). The analysis revealed when averaging across all tested emotions, bodily expression tended to attract higher categorization accuracy than facial expression. However, the two expression channels demonstrated category-dependent modification of dog emotion categorization accuracy (e.g., higher accuracy in recognizing facial expressions of anger and surprise, but bodily expressions of happiness and fear) and bias (e.g., mistaking fear facial expression as happiness, but fear bodily expression as sadness). Furthermore, the impact of owner experience on recognizing dog emotions was also modulated by the expression channel and emotion category (e.g., prolonged experience with dogs tended to improve performance in recognizing the fear facial expression, and the appeasement bodily expression). Taken together, these results suggest that different channels of emotional expression by dogs may transmit category-specific diagnostic emotional cues, which aid human appraisal of their affective states.

Advancements in Emotion Classification via Facial and Body Gesture Analysis: A Survey

ABSTRACTMachine vision has had a substantial impact on human‐computer interaction and psychological research. The ability of robots to generate realistic facial expressions has been enhanced by advancements in artificial intelligence, neural networks, and deep learning, thereby nurturing more profound emotive connections between humans and machines. This paper offers an in‐depth look of the most recent developments in emotion classification technologies, with a specific focus on the period from 2019 to 2023. This research addresses the integration of facial and body gesture analysis into emotion detection, emphasising the development of methods such as partial transfer learning, sign‐based measurement systems, and lightweight convolutional neural networks. By analysing the most recent research, the paper not only provides a comprehensive overview of the most advanced methods, but also emphasises their superiority over previous approaches in terms of efficiency, and applicability. The integration of gestures into emotion analysis is underscored as a critical area for further investigation, providing novel opportunities for comprehending and interpreting the intricate layers of nonverbal communication. The objective of this survey is to provide a resource for researchers and practitioners who are interested in advancing the field of emotion classification by utilising innovative methodologies.

Hybrid deep models for parallel feature extraction and enhanced emotion state classification

Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results. This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality reduction via Principal Component Analysis (PCA). The reduced features are then classified using a multi-class Support Vector Machine (SVM) to categorize different types of emotions. The proposed model was evaluated using the DEAP and EEG Brainwave datasets, both well-suited for emotion analysis due to their comprehensive EEG signal recordings and diverse emotional stimuli. The DEAP dataset includes EEG signals from 32 participants who watched 40 one-minute music videos, while the EEG Brainwave dataset categorizes emotions into positive, negative, and neutral based on EEG recordings from participants exposed to six different film clips. The proposed model achieved an accuracy of 95.54% and 97.26% for valence and arousal categories in the DEAP dataset, respectively, and 98.42% for the EEG Brainwave dataset. These results significantly outperform existing methods, demonstrating the model’s superior performance in terms of precision, recall, F1-score, specificity, and Mathew correlation coefficient. The integration of AlexNet and DenseNet, combined with PCA and multi-class SVM, makes this approach particularly effective for capturing the intricate patterns in EEG data, highlighting its potential for applications in human-computer interaction and mental health monitoring, marking a significant advancement over traditional methods.

Machine Learning Techniques Towards Accurate Emotion Classification from EEG Signals

This article delves into using machine learning algorithms for emotion classification via EEG brain signals. The goal is to discover an accurate model beyond traditional methods, necessitating AI for classifying emotional EEG signals. This study, motivated by the complex link between emotions and neural activity, employs Random Forest, Support Vector Machines, and K-Nearest Neighbors. Notably, Random Forest achieves 99% accuracy, SVM 98%, and KNN 94%. These impressive results, backed by performance metrics like confusion matrices, reveal each model’s effectiveness in emotion classification. The dataset, rich in varied emotional stimuli and EEG placements, provides a robust foundation for detailed analysis. This research underscores significant applications in affective computing and mental health, offering a promising path to understanding the intricate relationship between EEG signals and human emotions.

Hybrid Model for Human Behavior Recognition Using Emotions, Sentiments, and Mood Features

While social networking is a powerful communication tool, the obscured behavior of individuals on social networks remains a significant problem for users. Currently, research work is being focused on formulating mechanisms to determine the obscured behavior of users for secure and trustworthy social media. The proposed model employs mathematical formulation and multinomial classification of mood and emotions to analyze the conduct of an individual, thus enabling social trust on social media. First, natural language processing techniques are applied to predict the emotions, moods, and sentiments of an individual from the text, and then a mathematical model is applied to gather a comprehensive picture of one’s behavior using calculations at numerous instants. Finally, a subsequent trust state log is built in terms of positive and negative states which show the devotion in behavior in terms of mood, sentiments, and more significantly emotions. The efficiency of the proposed work has been demonstrated using simulation-based and real-world datasets along with individual behavior graphs for various conversations

Design of pattern image generation technique based on self-attentive residual conditional generative adversarial network

Image generation techniques have made remarkable progress in the digital image processing and computer vision. However, traditional generation models cannot meet the complexity and diversity requirements of patterned images. In view of this, the study aims to enhance the quality of generated pattern images, which uses improved residual block, and introduces a self-attention mechanism to compute the weight parameters of the input features to enhance the accuracy. Comparing with three image generation models, the research model shows lower Frechette initial distance, which is better than the other three methods, and the average Frechette initial distance values in the four scenes are 175.23, 176.41, 174.41, and 165.23. Generated mouths and eyes: the average values of Frechette initial distances reach 98.23 and 97.24, respectively. For emotion classification, the Frechette initial distance averages for sad, excited, and calm emotions were 82.34, 75.63, and 70.21, respectively. The model was trained up to 2500 iterations, and the loss value was reduced to 0.54, with an accuracy of 98.23%, confirming its effectiveness and high performance. The self attention residual network enhances the model's ability to capture image details, effectively improving the quality and accuracy of image generation, and providing a new technological path for radiation imaging data processing and analysis in radiation science.

Effect of Age and Gender on Categorical Perception of Vocal Emotion Under Tonal Language Background.

Categorical perception (CP) manifests in various aspects of human cognition. While there is mounting evidence for CP in facial emotions, CP in vocal emotions remains understudied. The current study attempted to test whether individuals with a tonal language background perceive vocal emotions categorically and to examine how factors such as gender and age influence the plasticity of these perceptual categories. This study examined the identification and discrimination performance of 24 Mandarin-speaking children (14 boys and 10 girls) and 32 adults (16 males and 16 females) when they were presented with three vocal emotion continua. Speech stimuli in each continuum consisted of 11 resynthesized Mandarin disyllabic words. CP phenomena were detected when Mandarin participants perceived vocal emotions. We further found the modulating effect of age and gender in vocal emotion categorization. Our results demonstrate for the first time that a categorical strategy is used by Mandarin speakers when perceiving vocal emotions. Furthermore, our findings reveal that the categorization ability of vocal emotions follows a prolonged course of development and the maturation patterns differ across genders. This study opens a promising line of research for investigating how sensory features are mapped to higher order perception and provides implications for our understanding of clinical populations characterized by altered emotional processing. https://doi.org/10.23641/asha.27204057.

Emotion Classification from Speech Waveform Using Machine Learning and Deep Learning Techniques

Emotions play a key role in determining the human mental state and indirectly express an individual’s well- being. A speech emotion recognition system can extract a person’s emotions from his/her speech inputs. There are some universal emotions such as anger, disgust, fear, happiness, pleasantness, sadness and neutral. These emotions are of significance especially in a situation like the Covid pandemic, when the aged or sick are vulnerable to depression. In the current paper, we examined various classification models with finite computational strength and resources in order to determine the emotion of a person from his/her speech. Speech prosodic features like pitch, loudness, and tone of speech, and work spectral features such as Mel Frequency Capstral Coefficients (MFCCs) of the voice were used to analyze the emotions of a person. Although sequence to sequence state of the art models for speech detection that offer high levels of accuracy and precision are currently in use, the computational needs of such approaches are high and inefficient. Therefore, in this work, we emphasised analysis and comparison of different classification algorithms such as multi layer perceptron, decision tree, support vector machine, and deep neural networks such as convolutional neural network and long short term memory. Given an audio file, the emotions that were exhibited by the speaker were recognized using machine learning and deep learning techniques. A comparative study was performed to identify the most appropriate algorithms that could be used to recognize emotions. Based on the experiment results, the MLP classifier and convolutional neural network model offered better accuracy with smaller variations when compared with other models used for the study.