Text Emotion Research Articles

A dual framework for implicit and explicit emotion recognition: An ensemble of language models and computational linguistics

One of the research domains in the field of sentiment analysis is automatic emotion recognition in texts which is a worthy topic in human-computer interaction. Text processing has always faced many challenges. The main one is the structural and semantic differences of sentences which have had a significant impact on the malfunction of auto-recognition systems. This problem becomes more prominent in short texts in which words and their concurrences are limited and insufficient. As a result of this, word frequency and TF-IDF weighing cannot well represent the relationship between words and the appropriate feature vector, leading to an undesirable accuracy of emotion recognition. Thus, different strategies should be applied to improve the feature vector and to formulate the features properly. The desired strategy should be able to identify the words that can distinguish between classes well and also to find the relationships between words and meaningful phrases using natural language processing concepts. In this paper, a combination of emotional models, categorical and hierarchical, are used for an emotional text recognition which could discover simultaneously explicit and implicit emotion in a short text. Our approach called DuFER, proposed a weighed method which improves the feature vector using language models and computational linguistics through applying a modified TF-IDF weighing to words as well as Maximum Likelihood Estimation weighing to expressions. Four implicit and explicit emotion datasets are used for the experiments. The results show that the accuracy of both implicit and explicit emotion recognition has increased and DuFER is actually the first successful dual framework in recognizing implicit and explicit emotions from text.

Chinese Language Literature Emotion Analysis Model Based on Unbalanced K-Nearest Neighbor Classification Method

The Chinese language is a nation’s symbol, the accumulation of a country’s magnificent culture, and the pearl precipitated in the long history’s washing. The Chinese language is rich and complex, and there are still many topics and issues that merit repeated exchanges and discussions in academic circles. This study proposes a classification method of emotion polarity based on reliability analysis in order to identify the tendency of literary emotion in Chinese language. Support vector machine (SVM), class center, and KNN (K-nearest neighbor) are included in the combined classifier, which effectively improves the accuracy and efficiency of emotion polarity classification. A Chinese literary emotion analysis model based on the method of UKNNC (unbalanced K-nearest neighbor classification) is proposed at the same time by analysing the characteristics of text structure and emotion expression. The experimental results show that, when compared to traditional machine methods, the UKNNC method can analyse text sentiment in fine-grained and multilevel ways, while also improving the accuracy of Chinese literary sentiment analysis.

The role of using the language of feelings and emotions in mass media texts

This article examines the phenomenon of feeling, defines how it manifests in the linguistic aspect of the person and analyzes the types of emotional aspects found in  mass media texts. Research purpose is the comparative study of linguo-cultural characteristics of emotional vocabulary in Kazakh and English. The main goals and objectives of scientific research are as follows: to determine the volume and composition; to define the lexical and semantic features; to reveal the national-cultural function; to determine cultural-national connotation;  to address issues of national acculturation of words that express feelings in English and Kazakh mass media texts. The functions of language are considered in conjunction with its sensory functions, the meaning of language as the general tool for this purpose is comprehensively revealed. A person's worldview is defined, different emotional states of feeling, the world of art and the language of national identity are presented. The findings of the study broaden the possibilities for presenting new knowledge of the world through the media. A significant research result is the study process of the features of the expression of feelings and emotions. Research methods used: content analysis, continuous sampling method, method of linguistic analysis. Research value is the importance and scientific significance of comparative analyses in English and Kazakh feelings vocabulary, which is a new and quite promising direction of linguistics. The study of this phenomenon gives writers, poets, linguists, journalists and other creative professionals the prerequisite for the development of feelings vocabulary. Key words: feeling, emotions, phenomena, mass media texts, language aspects, genres, the notion of feelings.

Deep learning neural networks for emotion classification from text: enhanced leaky rectified linear unit activation and weighted loss

Accurate emotion classification for online reviews is vital for business organizations to gain deeper insights into markets. Although deep learning has been successfully implemented in this area, accuracy and processing time are still major problems preventing it from reaching its full potential. This paper proposes an Enhanced Leaky Rectified Linear Unit activation and Weighted Loss (ELReLUWL) algorithm for enhanced text emotion classification and faster parameter convergence speed. This algorithm includes the definition of the inflection point and the slope for inputs on the left side of the inflection point to avoid gradient saturation. It also considers the weight of samples belonging to each class to compensate for the influence of data imbalance. Convolutional Neural Network (CNN) combined with the proposed algorithm to increase the classification accuracy and decrease the processing time by eliminating the gradient saturation problem and minimizing the negative effect of data imbalance, demonstrated on a binary sentiment problem. The results show that the proposed solution achieves better classification performance in different data scenarios and different review types. The proposed model takes less convergence time to achieve model optimization with seven epochs against the current convergence time of 11.5 epochs on average. The proposed solution improves accuracy and reduces the processing time of text emotion classification. The solution provides an average class accuracy of 96.63% against a current average accuracy of 91.56%. It also provides a processing time of 23.3 milliseconds compared to the current average processing time of 33.2 milliseconds. Finally, this study solves the issues of gradient saturation and data imbalance. It enhances overall average class accuracy and decreases processing time.

Is my doctor around me? Investigating the impact of doctors’ presence on patients’ review behaviors on an online health platform

AbstractPatient‐generated online reviews are well‐established as an important source of information for people to evaluate doctors' quality and improve health outcomes. However, how such reviews are generated in the first place is not well examined. This study examines a hitherto unexplored social driver of online review generation—doctors' presence on online health platforms, which results in the reviewers (i.e., patients) and the reviewees (i.e., doctors) coexisting in the same medium. Drawing on the Stimulus‐Organism‐Response theory as an overarching framework, we advance hypotheses about the impact of doctors' presence on their patients' review behaviors, including review volume, review effort, and emotional expression. To achieve causal identification, we conduct a quasi‐experiment on a large online health platform and employ propensity score matching and difference‐in‐difference estimation. Our findings show that doctors' presence increases their patients' review volume. Furthermore, doctors' presence motivates their patients to exert greater effort and express more positive emotions in the review text. The results also show that the presence of doctors with higher professional titles has a stronger effect on review volume than the presence of doctors with lower professional titles. Our findings offer important implications both for research and practice.

Text emotion classification system based on multifractal methods

Emotion classification is an attractive research, emotion classification based on machine learning has broad application prospects in the field of computer. It is particularly important to construct an effective and accurate algorithm to extract emotional features from subjective texts, and classify them into corresponding emotional categories. In this paper, we implement a novel classification system for identifying emotions in a positive and negative corpus dataset. The corpus contains hotel reviews of Chinese emotion mining, and we choose 1000 positive and 1000 negative corpus as the dataset for analysis. We first preprocess the positive and negative texts, and after obtaining the characteristic word text of positive and negative corpus, we use Word2Vec model to transform the sentence into a numerical vector. We regard the obtained feature word vector as a time series, and adopt three multifractal analysis on the time series, such as multifractal detrended fluctuation analysis (MF-DFA), Multifractal detrended moving average (MF-DMA), and Multifractal detrended weighted average algorithm of historical volatility (MF-DHV). After extracting the Hurst exponents of the vector time series, we put the features as the input vector into SVM for classification analysis. We utilize four key values such as Accuracy (Acc), Precision (P), Recall (R) and F-score (F1) to evaluate the classification experiments. The results suggest that Word2Vec-MF-DHV-SVM model is more effective and competitive in extracting information from corpus numerical vector.

DC-BiGRU-CNN Algorithm for Irony Recognition in Chinese Social Comments

Irony recognition is an important research direction in text sentiment analysis, which contributes to discover ironic tone to judge text emotion correctly. The object of this paper is the application of deep learning to irony recognition, and the research aims to solve the problem that existing machine learning algorithms have difficulty in discriminating ironic tones. Aiming at the irony recognition of Chinese social comments, DC-BiGRU-CNN is proposed from the perspective of structural optimization rather than a text vectorization mechanism point of view, which is a dual-channel CNN combined with BiGRU, and incorporates attention mechanism and multigranularity convolutional neural network as the main framework. This paper first briefly discusses the difficulties of irony recognition and provides an overview of existing text sentiment analysis algorithms. This is followed by a detailed discussion of DC-BiGRU-CNN. Furtherly, it is compared with the main irony recognition methods on a social comment dataset containing Chinese ironic comments, and the experimental results show that DC-BiGRU-CNN can improve the accuracy of irony recognition.

Emotional Analysis Model for Social Hot Topics of Professional Migrant Workers.

Text makes up a large portion of network data because it is the vehicle for people's direct expression of emotions and opinions. How to analyze and mine these emotional text data has become a hot topic of concern in academia and industry in recent years. The online LDA (Latent Dirichlet Allocation) model is used in this paper to train the social hot topic data of professional migrant workers on the same time slice, and the subtopic evolution and intensity are obtained. The topic development is divided into four categories, and the classification model is created using SVM (Support Vector Machine). Instead of decision makers, a virtual human with sensibility and rationality is built using a hierarchical emotional cognitive model to solve multiobjective optimization problems interactively. It analyzes human body structure and emotional signals, and then combines them with visual and physiological signals to create multimodal emotional data. An example is used to demonstrate the effectiveness of the proposed model.

EmoMix+: An Approach of Depression Detection Based on Emotion Lexicon for Mobile Application

Emotion lexicon is an important auxiliary resource for text emotion analysis. Previous works mainly focused on positive and negative classification and less on fine-grained emotion classification. Researchers use lexicon-based methods to find that patients with depression express more negative emotions on social media. Emotional characteristics are an effective feature in detecting depression, but the traditional emotion lexicon has limitations in detecting depression and ignores many depression words. Therefore, we build an emotion lexicon for depression to further study the differences between healthy users and patients with depression. The experimental results show that the depression lexicon constructed in this paper is effective and has a better effect of classifying users with depression.

Long-Term Influence of Incidental Emotions on the Emotional Judgment of Neutral Faces.

Background: Evidence indicates that the processing of facial stimuli may be influenced by incidental factors, and these influences are particularly powerful when facial expressions are ambiguous, such as neutral faces. However, limited research investigated whether emotional contextual information presented in a preceding and unrelated experiment could be pervasively carried over to another experiment to modulate neutral face processing.Objective: The present study aims to investigate whether an emotional text presented in a first experiment could generate negative emotion toward neutral faces in a second experiment unrelated to the previous experiment.Methods: Ninety-nine students (all women) were randomly assigned to read and evaluate a negative text (negative context) or a neutral text (neutral text) in the first experiment. In the subsequent second experiment, the participants performed the following two tasks: (1) an attentional task in which neutral faces were presented as distractors and (2) a task involving the emotional judgment of neutral faces.Results: The results show that compared to the neutral context, in the negative context, the participants rated more faces as negative. No significant result was found in the attentional task.Conclusion: Our study demonstrates that incidental emotional information available in a previous experiment can increase participants’ propensity to interpret neutral faces as more negative when emotional information is directly evaluated. Therefore, the present study adds important evidence to the literature suggesting that our behavior and actions are modulated by previous information in an incidental or low perceived way similar to what occurs in everyday life, thereby modulating our judgments and emotions.

Research on the Influence of Visual Factors on Emotion Regulation Interaction.

To guide the design direction of emotion regulation products that improve the positive emotions of users, investigation into the correlation between relevant visual factors and multi-dimensional complex emotions is needed. In the present study, an extended product emotion measurement method was adopted to describe the multi-dimensional emotional set of each influencing factor and calculate their weight according to the order. The positive and negative emotion indicators of all influencing factors were compared and the evaluation and ranking factors that affect users’ emotional value of emotion regulation products were analyzed. The experimental results reveal that specific emotion mapping scenes on positive emotion are the most significant among the key factors affecting user emotion. Further, the influence of emotional stickers, interactive data visualization, and text on positive emotions decreased in turn. The influence of emotional text on positive emotion was the lowest. Through investigating the visual factors that affect the psychological emotions of users, the development of emotion regulating products could be guided in a more scientific and reasonable manner.

Question Text Classification Method of Tourism Based on Deep Learning Model

The Internet of Things applications are diverse in nature, and a key aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). Compared with the Internet of Things, it generates a large amount of text data with different characteristics and requirements. Aiming at the problems that machine learning and single structure deep learning model cannot effectively grasp the text emotional information in text processing, resulting in poor classification effect, this paper proposes a text classification method of tourism questions based on deep learning model. First, the corpus is trained with word2vec tool based on continuous word bag model to obtain the text word vector representation. Then, the attention mechanism is introduced into the long-short term network (LSTM), and the attention-based LSTM model is constructed for text feature extraction, which highlights the impact of different words in the input text on the text emotion category. Finally, the text features are input into the Softmax classifier to obtain the probability distribution of text categories, and the model is trained combined with the cross entropy loss function. The experimental results show that the average accuracy, recall, and F value are 0.943, 0.867, and 0.903, respectively, which has better classification effect than other methods.

Text emotion recognition using fast text word embedding in bi-directional gated recurrent unit

Emotions are states of readiness in the mind that result from evaluations of one's own thinking or events. Although almost all of the important events in our lives are marked by emotions, the nature, causes, and effects of emotions are some of the least understood parts of the human experience. Emotion recognition is playing a promising role in the domains of human-computer interaction and artificial intelligence. A human's emotions can be detected using a variety of methods, including facial gestures, blood pressure, body movements, heart rate, and textual data. From an application standpoint, the ability to identify human emotions in text is becoming more and more crucial in computational linguistics. In this work, we present a classification methodology based on deep neural networks. The Bi-directional Gated Recurrent Unit (Bi-GRU) employed here demonstrates its effectiveness on the Multimodal Emotion Lines Dataset (MELD) when compared to Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM). For word encoding, a comparison of three pre-trained word embeddings namely Glove, Word2Vec, and fastText is made. The findings from the MELD corpus support the conclusion that fastText is the best word embedding for the proposed Bi-GRU model. The experiment utilized the "glove.6B.300d" vector space. It consists of two million word representations in 300 dimensions trained on Common Crawl with sub-word information (600 billion tokens). The accuracy scores of GloVe, Word2Vec, and fastText (300 dimensions each) are tabulated and studied in order to highlight the improved results with fastText on the MELD dataset tested. It is observed that the Bidirectional Gated Recurrent Unit (Bi-GRU) with fastText word embedding outperforms GloVe and Word2Vec with an accuracy of 79.7%.

Emotion Detection from Text and Sentiment Analysis of Ukraine Russia War using Machine Learning Technique

In the human body, emotion plays a critical func-tion. Emotion is the most significant subject in human-machine interaction. In economic contexts, emotion detection is equally essential. Emotion detection is crucial in making any decision. Several approaches were explored to determine emotion in text. People increasingly use social media to share their views, and researchers strive to decipher emotions from this medium. There has been some work on emotion detection from the text and sentiment analysis. Although some work has been done in which emotion has been recognized, there are many things to improve. There is not much work to detect racism and analysis sentiment on Ukraine -Russia war. We suggested a unique technique in which emotion is identified, and the sentiment is analyzed. We utilized Twitter data to analyze the sentiment of the Ukraine-Russia war. Our system performs better than prior work. The study increases the accuracy of detecting emotion. To identify emotion and racism, we used classical machine learning and the ensemble method. An unsupervised approach and NLP modules were used to analyze sentiment. The goal of the study is to detect emotion and racism and also analyze the sentiment.

Mormonism and the Emotions: An Analysis of LDS Scriptural Texts

Mormonism and the Emotions: An Analysis of LDS Scriptural Texts

Review: Evocative Autoethnography: Writing Lives and Telling Stories, by Arthur P. Bochner and Carolyn Ellis

Review: <i>Evocative Autoethnography: Writing Lives and Telling Stories</i>, by Arthur P. Bochner and Carolyn Ellis

Network Sentiment Analysis of College Students in Different Epidemic Stages Based on Text Clustering.

In order to analyze the evolution trend of public opinion in emergencies and explore its evolution law, this paper constructs a network sentiment analysis model based on text clustering, where the emotion analysis part is based on the pretraining BERT model and BiGRU model, in which BERT is used as the word embedding model to extract the feature vector of emotional text and BiGRU is used to extract the context of the text feature vector to accurately identify the sentiment polarity of public opinion data. In addition, the K-means clustering algorithm and Kolmogorov-Smirnov Z test were used to divide the different epidemic stages. Compared with other methods, the model proposed in this paper has a great degree of improvement in accuracy, recall, and F1 score index, which provides an opportunity reference and effective detection means for schools at all levels to carry out timely mental health education and psychological intervention for students.

The Psychosocial Dimension of Power: An Emotional Analysis of the Discourse on Globalization of the Davos Elite

This work focused on the psychosocial dynamics underlying the political and cultural action of the global elite, through a pilot study of the image of globalization of the World Economic Forum (WEF) addressed to identify possible strategies for developing those dynamics in a more democratic and socio-economically fair way in order to strengthen the global civil coexistence. The study was conducted on a corpus of texts composed of interviews, articles and speeches of the member of World Economic Forum’s board of trustees focused on the issue of globalization, according to the methodology of Emotional Text Analysis. The results show the centrality of specific patterns of relational and organizational models, based on the tendency to possess the other, the need for power as dominant social motivation and a self-referential, mythical conception of supranational financial institutions which let them to operate without real commission, nor evaluation of efficacy, from their beneficiaries. On the basis of this scenario, two ways of development were identified: the valorization of the other as a resource for the reciprocal development and the passage from a compliance based logic of action to a commonly agreed goals and products based one.

Aesthetic and Implication Analysis of the Traditional Poetic Environment Based on Natural Language Emotion Analysis.

The uniqueness of aesthetic implication in Zhou Dynasty poetics lies in that it is the basic forming stage of the concept of formal beauty of the whole Chinese nation, and the aesthetic implication of the Zhou Dynasty poetics art has fundamental significance for the whole ancient Chinese aesthetic implication theory. In the discipline of natural language processing, text emotion analysis is a crucial topic. Artificial neural network research is where the idea of “deep learning” (DL) first emerged. In view of the problems that semantic information is easy to be lost and emotional information may be ignored in the traditional Chinese short text emotion analysis model, this paper introduces the AM (attention mechanism) and proposes a CNN-LSTM (convolutional neural network-long short-term memory) poetic aesthetic implication analysis method based on self-attention. For the IL (input layer), word vectors trained by Word2Vec are used and then input into the CNN-LSTM joint model. Then, the output of the joint model is weighted and summed by self-attention and finally input into the Softmax classifier, so as to realize the emotion classification of the text. By creating and putting into practise pertinent comparative experiments, the usefulness of the proposed model is confirmed. The outcomes demonstrate that this model outperforms the other three comparison models for the quantification of evaluation indices in terms of overall performance. The accuracy and F1 of this paper are 93.362% and 90.886%, respectively, which are higher than other models.

Rating the Crisis of Online Public Opinion Using a Multi-Level Index System

Online public opinion usually spreads rapidly and widely, thus a small incident probably evolves into a large social crisis in a very short time, and results in a heavy loss in credit or economic aspects. We propose a method to rate the crisis of online public opinion based on a multi-level index system to evaluate the impact of events objectively. Firstly, the dissemination mechanism of online public opinion is explained from the perspective of information ecology. According to the mechanism, some evaluation indexes are selected through correlation analysis and principal component analysis. Then, a classification model of text emotion is created via the training by deep learning to achieve the accurate quantification of the emotional indexes in the index system. Finally, based on the multi-level evaluation index system and grey correlation analysis, we propose a method to rate the crisis of online public opinion. The experiment with the real-time incident show that this method can objectively evaluate the emotional tendency of Internet users and rate the crisis in different dissemination stages of online public opinion. It is helpful to realizing the crisis warning of online public opinion and timely blocking the further spread of the crisis.