Emotion Analysis Method Research Articles

Enhancing Place Emotion Analysis with Multi-View Emotion Recognition from Geo-Tagged Photos: A Global Tourist Attraction Perspective

User−generated geo−tagged photos (UGPs) have emerged as a valuable tool for analyzing large−scale tourist place emotions with unprecedented detail. This process involves extracting and analyzing human emotions associated with specific locations. However, previous studies have been limited to analyzing individual faces in the UGPs. This approach falls short of representing the contextual scene characteristics, such as environmental elements and overall scene context, which may contain implicit emotional knowledge. To address this issue, we propose an innovative computational framework for global tourist place emotion analysis leveraging UGPs. Specifically, we first introduce a Multi−view Graph Fusion Network (M−GFN) to effectively recognize multi−view emotions from UGPs, considering crowd emotions and scene implicit sentiment. After that, we designed an attraction−specific emotion index (AEI) to quantitatively measure place emotions based on the identified multi−view emotions at various tourist attractions with place types. Complementing the AEI, we employ the emotion intensity index (EII) and Pearson correlation coefficient (PCC) to deepen the exploration of the association between attraction types and place emotions. The synergy of AEI, EII, and PCC allows comprehensive attraction−specific place emotion extraction, enhancing the overall quality of tourist place emotion analysis. Extensive experiments demonstrate that our framework enhances existing place emotion analysis methods, and the M−GFN outperforms state−of−the−art emotion recognition methods. Our framework can be adapted for various geo−emotion analysis tasks, like recognizing and regulating workplace emotions, underscoring the intrinsic link between emotions and geographic contexts.

Coupled Multimodal Emotional Feature Analysis Based on Broad-Deep Fusion Networks in Human-Robot Interaction.

A coupled multimodal emotional feature analysis (CMEFA) method based on broad-deep fusion networks, which divide multimodal emotion recognition into two layers, is proposed. First, facial emotional features and gesture emotional features are extracted using the broad and deep learning fusion network (BDFN). Considering that the bi-modal emotion is not completely independent of each other, canonical correlation analysis (CCA) is used to analyze and extract the correlation between the emotion features, and a coupling network is established for emotion recognition of the extracted bi-modal features. Both simulation and application experiments are completed. According to the simulation experiments completed on the bimodal face and body gesture database (FABO), the recognition rate of the proposed method has increased by 1.15% compared to that of the support vector machine recursive feature elimination (SVMRFE) (without considering the unbalanced contribution of features). Moreover, by using the proposed method, the multimodal recognition rate is 21.22%, 2.65%, 1.61%, 1.54%, and 0.20% higher than those of the fuzzy deep neural network with sparse autoencoder (FDNNSA), ResNet-101 + GFK, C3D + MCB + DBN, the hierarchical classification fusion strategy (HCFS), and cross-channel convolutional neural network (CCCNN), respectively. In addition, preliminary application experiments are carried out on our developed emotional social robot system, where emotional robot recognizes the emotions of eight volunteers based on their facial expressions and body gestures.

Immersive Emotion Analysis in VR Environments: A Sensor-Based Approach to Prevent Distortion

As virtual reality (VR) technology advances, research has focused on enhancing VR content for a more realistic user experience. Traditional emotion analysis relies on surveys, but they suffer from delayed responses and decreased immersion, leading to distorted results. To overcome these limitations, we propose an emotion analysis method using sensor data in the VR environment. Our approach can take advantage of the user’s immediate response and not reduce immersion. Linear regression, classification analysis, and tree-based methods were applied to electrocardiogram and galvanic skin response (GSR) sensor data to measure valence and arousal values. We introduced a novel emotional dimension model by analyzing correlations between emotions and the valence and arousal values. Experimental results demonstrated the highest accuracy of 77% and 92.3% for valence and arousal prediction, respectively, using GSR sensor data. Furthermore, an accuracy of 80.25% was achieved in predicting valence and arousal using nine emotions. Our proposed model improves VR content through more accurate emotion analysis in a VR environment, which can be useful for targeting customers in various industries, such as marketing, gaming, education, and healthcare.

Understanding Information Literacy Engagement Patterns of Generation Z

This study aims to verify the hypothesis that Information Literacy is a key course in the professional development of students. The authors review the obstacles, interests, and goals of students after their completion of the subject in Information Literacy. Quantitative survey-based research was conducted online at Transilvania University of Brasov. The literature was revised by scientometric methods in order to analyse the most relevant and highly cited articles published in Web of Science. Emotion Analysis method was used, since it is a fast method to scan students’ feelings. Students from Generation Z, the digital native nowadays, have remarkable digital skills and use information sources mainly from the digital environment. In this respect, Informational Literacy courses need to keep pace with the mentality and information behaviour of new generations. The hypothesis was thus supported; the students need this course because subjects like Copyright, Search Strategies, and Databases are of interest. Moreover, these informational research skills are important in professional development. This critical evaluation of Generation Z education may interest those studying trends in education and students’ needs.

Long-term and short-term memory network based movie comment sentiment analysis

This paper proposes an emotional analysis method of movie reviews based on Long-term and Short-term Memory(LSTM) Network model. Emotional analysis is widely used in movie recommendation system, which can recommend and judge movies by understanding the audiences emotional response to movies. However, due to the characteristics of movie text and the complexity of emotional expression, traditional methods such as machine learning have limitations and shortcomings in emotional analysis. However, the LSTM models better memory is utilized by the method proposed in this paper and the ability to capture the long-term correlation in movie texts, which obviously improves the accuracy and reliability of emotional analysis, and demonstrates the advantages of the LSTM model in emotional analysis compared to the traditional model. Future research can further explore other deep learning models and algorithms, so as to make emotional analysis more accurate and provide users with reliable movie recommendation information.

Natural language processing-based machine learning psychological emotion analysis method

Natural language processing-based machine learning psychological emotion analysis method

Natural language processing based machine learning psychological emotion analysis method

Natural language processing based machine learning psychological emotion analysis method

텍스트마이닝을 활용한 부동산 시장 인식 변화 연구

This study compared and analyzed real estate market trends over the last six years using social big data generated on the web and social media. To this end, the applicability of social network analysis as a method of confirming and understanding public perception of the real estate market was confirmed. Big data were used for analysis, and because keyword network analysis was at the heart of the study, the author's patented “emotional information analysis method” was partially used. The analysis result reveals that from 2017 to 2019, keywords related to the rising phase of the real estate market were derived with high influence, and real estate policy keywords were ranked highly. Meanwhile, from 2020 to 2022, keywords related to the real estate market downturn had a high impact, and those related to real estate costs were ranked high. These research findings are a preliminary study that seeks an effective research methodology in understanding the past and present real estate markets. More specific policy research should be conducted using these research results.

Sentiment analysis method of consumer comment text based on BERT and hierarchical attention in e-commerce big data environment

Abstract This study proposes an emotional analysis method of consumer comment text based on Bidirectional Encoder Representations from Transformers (BERT) and hierarchical attention. First, using the BERT pre-training model, the left and right contextual information is fused to enhance the semantic representation of words and generate dynamic word vectors containing contextual semantics. Second, the bidirectional long short-term memory network is used to obtain the sequence feature matrix, and the sentence representation and the text representation are obtained using the two-layer long short-term memory. Finally, the local attention mechanism and the global attention mechanism are introduced into the sentence representation layer and the text representation layer, respectively, and the text emotion of consumer comments is classified by softmax. Experiments show that the accuracy of the proposed method in Laptop data set is 93.01% and that in Restaurant data set is 92.45%. Therefore, the performance of the proposed method in the emotional analysis of consumer comment text is significantly better than that of the comparison method.

Emotion Analysis on Youtube Comments for 2023 Turkish Presidential Elections

The 13th Presidential election has created a wide agenda in many countries as well as in Turkey. In this election period, along with traditional media tools, social media tools were also used frequently in the execution of election campaigns. Interactions received through social media platforms once again proved the effective power of social media tools to reach large masses of all parties and party leaders. For this reason, the Open Microphone program organized by Oğuzhan Uğur, in which many politicians participated, was followed with interest not only in Turkey's agenda, but also in the world's agenda. In this context, this study aims to reveal various analysis findings with Emotion Analysis methods, especially from the comments made within the scope of this program. For this purpose, in this study, a total of 261.728 user comments, specific to 7 different politicians, were analyzed using the NRC emotion dictionary. With the NRC emotion dictionary, a broader emotional polarity was obtained, including the emotions of anger, fear, trust, anticipation, surprise, sadness, joy, and disgust, in addition to positive or negative emotion polarity. As a result of the findings, this study reveals that the emotion analysis of the masses through Youtube comments or different platforms can be a critical source of information for political campaigns.

Image Emotion Analysis Combining Attention Mechanism and Multi-level Correlation

The development of social network has brought a large amount of image information, and the research on image emotion has gradually attracted wide attention. The current image emotion analysis methods based on multi-level features simply splice the features at each level and then classify the emotions, which not only ignores the correlation between features at different levels, but also ignores the synergistic effect between global features and local features. Therefore, this paper proposes an emotion model (MAML) based on mixed attention and multi-level dependence of images, which uses spatial and channel attention mechanisms to extract local emotion region features of images. Bi-directional Long Short Term Memory network (BiLSTM) is used to establish correlation between multi-level image global features. The experimental results of MAML model on artphoto and abstract data sets prove the validity of MAML model.

An artificial intelligence-based classifier for musical emotion expression in media education.

Music can serve as a potent tool for conveying emotions and regulating learners' moods, while the systematic application of emotional assessment can help to improve teaching efficiency. However, existing music emotion analysis methods based on Artificial Intelligence (AI) rely primarily on pre-marked content, such as lyrics and fail to adequately account for music signals' perception, transmission, and recognition. To address this limitation, this study first employs sound-level segmentation, data frame processing, and threshold determination to enable intelligent segmentation and recognition of notes. Next, based on the extracted audio features, a Radial Basis Function (RBF) model is utilized to construct a music emotion classifier. Finally, correlation feedback was used to label the classification results further and train the classifier. The study compares the music emotion classification method commonly used in Chinese music education with the Hevner emotion model. It identifies four emotion categories: Quiet, Happy, Sad, and Excited, to classify performers' emotions. The testing results demonstrate that audio feature recognition time is a mere 0.004 min, with an accuracy rate of over 95%. Furthermore, classifying performers' emotions based on audio features is consistent with conventional human cognition.

Investigating the attitude and perspectives of Indian citizens toward COVID-19 vaccines: A text analytics study

Background and aimsThe COVID-19 pandemic has created havoc and an unprecedented global health crisis worldwide. In order to minimize the COVID-19 severity, the government of India started its mass vaccination program on January 16, 2021. This study attempts to investigate the attitude, viewpoints, and concerns for different types of COVID-19 vaccines voiced in social media by Indian citizens using text analytics. MethodsThe study applied sentiment and emotion analysis methods on social media posts to uncover the attitude and perspectives of Indian citizens toward COVID-19 vaccines, namely “Covishield” and “Covaxin”. Furthermore, topic modeling was performed to gain insights about issues and concerns associated with different COVID-19 vaccines. ResultsSentiment analysis findings report that the majority of posts related to COVID-19 vaccines posted in social media by Indian citizens were in a neutral tone followed by a positive tone. In addition, findings of emotion analysis highlight that ‘Surprise’ was the noticeable emotion toward “Covaxin”, while ‘Sad’ and ‘Fear’ emotions were related to “Covishield”. Further, topic modeling results show that ‘vaccine effect’, ‘vaccine efficacy’, and ‘government's vaccine action plan’ are the key topics of concern toward both COVID-19 vaccines. ConclusionsThe study recommends that Indian government organizations should identify the citizen's concerns voiced in social media for different types of COVID-19 vaccines and develop a proactive action plan to expedite the vaccination drive. Subsequently, it would help them to improve future vaccination drives.

An Effective Emotional Analysis Method of Consumer Comment Text Based on ALBERT-ATBiFRU-CNN

To address the challenges of insufficient feature extraction for text sentiment analysis in the e-commerce big data environment, the author proposes a deep learning-based emotion analysis method of consumer comment text. Firstly, the author obtained the contextualized word vectors by using a pretrained language model called A Lite Bidirectional Encoder Representations From Transformers (ALBERT). Secondly, the researcher used the bidirectional gate recurrent unit (BiGRU) model to capture the semantic information through the combination of positive and negative directions, measure the emotional polarity information of each text as a whole, and then catch the local characteristic information of the text using the convolutional neural network (CNN) model. Finally, the author calculated the weight distribution through the attention mechanism. The experiments on a publicly available consumer review dataset showed that the recall, precision, and F1-score of the proposed text emotion analysis method were 0.9417, 0.9552, and 0.9484, respectively, which are higher than the existing methods. Therefore, the proposed method is of great significance in capturing the emotions of consumers on e-commerce platforms.

Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data

Social media have been a valuable data source for studying people’s opinions, intentions, and behaviours. Such a data source incorporating advanced big data analysis methods, such as machine-operated emotion and sentiment analysis, will open unprecedented opportunities for innovative data-driven destination monitoring and management. However, a big challenge any machine-operated text analysis method faces is the ambiguity of the natural languages, which may cause an expression to have different meanings in different contexts. In this work, we address the ambiguity challenge by proposing a context-aware dictionary-based target-oriented emotion and sentiment analysis method that incorporates inputs from both humans and machines to introduce an alternative approach to measuring emotions and sentiment in limited tourism-related data. The study makes a methodological contribution by creating a target dictionary specifically for tourism sentiment analysis. To demonstrate the performance of the proposed method, a case of target-oriented emotion and sentiment analysis of posts from Twitter for the Gold Coast of Australia as a tourist destination was considered. The results suggest that Twitter data cover a broad range of destination attributes and can be a valuable source for comprehensive monitoring of tourist experiences at a destination.

The use of sentiment and emotion analysis and data science to assess the language of nutrition-, food- and cooking-related content on social media: a systematic scoping review.

Social media data are rapidly evolving and accessible, which presents opportunities for research. Data science techniques, such as sentiment or emotion analysis which analyse textual emotion, provide an opportunity to gather insight from social media. This paper describes a systematic scoping review of interdisciplinary evidence to explore how sentiment or emotion analysis methods alongside other data science methods have been used to examine nutrition, food and cooking social media content. A PRISMA search strategy was used to search nine electronic databases in November 2020 and January 2022. Of 7325 studies identified, thirty-six studies were selected from seventeen countries, and content was analysed thematically and summarised in an evidence table. Studies were published between 2014 and 2022 and used data from seven different social media platforms (Twitter, YouTube, Instagram, Reddit, Pinterest, Sina Weibo and mixed platforms). Five themes of research were identified: dietary patterns, cooking and recipes, diet and health, public health and nutrition and food in general. Papers developed a sentiment or emotion analysis tool or used available open-source tools. Accuracy to predict sentiment ranged from 33·33% (open-source engine) to 98·53% (engine developed for the study). The average proportion of sentiment was 38·8% positive, 46·6% neutral and 28·0% negative. Additional data science techniques used included topic modelling and network analysis. Future research requires optimising data extraction processes from social media platforms, the use of interdisciplinary teams to develop suitable and accurate methods for the subject and the use of complementary methods to gather deeper insights into these complex data.

Multimodal sentiment system and method based on CRNN-SVM

Traditional sentiment analysis focuses on text-level sentiment mining, transforming sentiment mining into classification or regression problems, resulting in a sentiment analysis low accuracy rate. Sentiment analysis refers to the use of natural language processing, text analysis, and computational linguistics to systematically identify, extract, quantify, and study sentimental states. Therefore, more scholars have begun to focus on speech recognition and facial expression recognition research, and extracting and analysing people’s sentiment tendencies can improve sentiment recognition accuracy. Traditional single-modal sentiment analysis can no longer meet people’s needs. Therefore, this paper proposes a multimodal sentiment analysis method based on the multimodal sentiment analysis method that can obtain more sentimental information sources and help people make better decisions. The experimental results in this paper show that the highest recognition rates of CNN-SVM, RNN-SVM, and CRNN-SVM were 76.8%, 71.2%, and 93.5%, respectively. It can be seen that CRNN-SVM has the highest sentiment tendency recognition rate in deep learning, so it is suitable to apply CRNN-SVM to sentiment tendency analysis system design in this paper. The average accuracy rate of the system designed in this paper was 91%, and the stability was also very strong, which shows that the system designed in this paper is meaningful. The main contribution of this paper is based on the limitations of single-mode emotion analysis. It proposes a multimode emotion analysis method and introduces a convolutional neural network to help people obtain more emotional information sources to meet their needs.

Crisis Management Experience from Social Media: Public Response to the Safety Crisis of Imported Aquatic Products in China during the Pandemic

China's outbreak related to cold-chain aquatic product quality and safety in 2020 caused public panic and further led to a crisis in China's aquatic industry. This paper uses topic clustering and emotion analysis methods to text-mine the comments of netizens on Sina Weibo to study the main features of the public's views on the administration's crisis management measures and to provide experience for future imported food safety management. The findings show that for the imported food safety incident and the risk of virus infection, the public response had four types of characteristics: a higher proportion of negative emotion; a wider range of information demand; attention paid to the whole imported food industry chain; and a differentiated attitude towards control policies. Based on the online public response, countermeasures to further improve the management ability of imported food safety crises are proposed as follows: the government should pay active attention to the development trend of online public opinion; work more on exploring the content of public concern and emotion; strengthen the risk assessment of imported food and establish the classification and management measures of imported food safety events; construct the imported food safety traceability system; build a special recall mechanism for imported food safety; and improve the cooperation between government and media, enhancing the public's trust in policies.

Microblog Emotion Analysis Using Improved DBN Under Spark Platform

In order to solve the problems that traditional single-machine methods find it difficult to complete the task of emotion classification quickly, and the time efficiency and scalability are not high; a microblog emotion analysis method using improved deep belief network (DBN) under Spark platform is proposed. First, the Hadoop distributed file system is used to realize the distributed storage of text data, and the preprocessed data and emotion dictionary are converted into word vector representation based on the continuous bag-of-words model. Then, an improved DBN model is constructed by combining the adaptive learning method of DBN with the active learning method, and it is applied to the learning analysis of text word vectors. Finally, the data parallel optimization of the improved DBN model is realized, based on Spark platform to accurately and quickly obtain the emotion types of microblog texts. The experimental analysis of the proposed method based on the microblog text data set shows that the classification accuracy is more than 94%.

An Emotional Analysis Method Based on Multi Model Ensemble Learning

<p>Traditional machine learning models generally use weak supervision model, which is difficult to adapt to the scene of multi classification for emotional text. Therefore, a multi model ensemble learning algorithm for emotional text classification is proposed. The algorithm takes the labeled emotional text data as the training sample, uses the improved TF-IDF algorithm to train the word vector space model, selects three weakly supervised machine learning algorithms, linear SVC, xgboost and logistic regression, to construct the base classifier, and uses the random forest algorithm to construct the meta classifier. It realizes the function of dividing emotional text into three categories: positive, neutral and negative. From the simulation and test results, the AUC values of the multi model ensemble learning algorithm model for each category are 0.93, 0.94 and 1.00, and the AP values are 0.87, 0.86 and 1.00, and the indicators of accuracy and recall are better than the single machine learning model, which realizes the high performance and high accuracy for emotional text classification.</p> <p> </p>