A Strategy for Network multi-layer Information Fusion Based on Multimodel in User Emotional Polarity Analysis

Combine the networking process and text emotion analysis technology to build an early warning system of public opinion, which provides a basis for judging the emotional tendency of police Weibo and points out the direction for the future emotional analysis and research. Based on the emotion analysis method of emotion dictionary, this paper constructs the analysis model of emotion polarity, takes the related topics of police work as an example to verify the availability of the model, and finally carries on the correlation analysis to these information of Weibo and the emotion polarity. The emotional polarity analysis model of this paper has availability, there is a significant positive correlation between Weibo comments and retweets, and when the retweets are low, there is a significant negative correlation between comments and emotional polarity. And when the number of likes on Weibo is greater than the number of comments, Weibo content itself has a positive emotional tendency.

In recent years, with the rapid development and wide application of the Internet, it has become the main place for the generation and dissemination of public opinion. To grasp the information of network public opinion in a timely and comprehensive way can not only effectively prevent sudden network malignant events but also provide a reference for the scientific and democratic decision-making of government departments. Therefore, in view of the practical application needs, this article studies the emotional characteristics and the evolution of public opinion over time based on the emotional feature words of network public opinion participants. Firstly, the positive and negative emotional lexicon of HowNet emotional dictionary is used, and the commonly used emotional lexicon and expression symbols are added to the lexicon. At the same time, the polarity annotation method of Chinese emotional lexicon ontology is used to construct the emotional lexicon of this article. Secondly, considering other emotional polarity characteristics in the dictionary, an emotional tendency analysis model is proposed. In this article, emotional analysis is applied to the evolution analysis of network public opinion, and the change of network public opinion characteristics with time series is obtained. The simulation results show that the emotional dictionary constructed in this article and the proposed model of emotional orientation analysis can effectively analyze the emotional characteristics of network public opinion participants and apply emotional analysis to the evolution analysis of network public opinion, which can get the change of emotional characteristics of public opinion participants with time series.

Accurate emotion analysis of teaching evaluation texts can help teachers effectively improve the quality of education and teaching. In order to improve the precision and accuracy of emotion analysis, this paper proposes an emotion recognition and analysis method based on deep learning model. First, LTP tool is used to effectively process the teaching evaluation texts data set to improve the completeness and reliability of the data. Based on bidirectional long short-term memory (BiLSTM) network, an emotion analysis model is constructed to enhance the long-term memory ability of the model, so as to learn the emotion feature information more fully. On the basis of this model, the attention interaction mechanism module is introduced to pay attention to the important information in the attribute sequence, mine the deeper emotion feature information, and further ensure the accuracy of emotion recognition of teaching evaluation texts. Experimental simulation results show that the accuracy and precision of emotion recognition of the proposed method are 0.9123 and 0.8214, which can meet the needs of accurate emotion analysis of complex teaching evaluation texts.

Deep learning (DL)-based natural language processing (NLP) technologies are applied and optimized for the analysis of emotional implications in text data. Understanding emotional intricacies in text has become necessary for a variety of industries, like social media analysis, customer feedback interpretation, and mental health monitoring, due to the exponential rise of digital material. The study emphasizes the difficulties associated with the automation of emotion analysis, including the diversities of the text formats and the NLP complexities. This research gathers that text-based emotion analysis data and pre-processing techniques are utilized for the analysis of emotion, including normalizing methods such as tokenization and lemmatization, along with the removal of stop words and punctuation. The model incorporates pre-trained embeddings and attention mechanisms to capture contextual information and emotional subtleties for any language. The study proposes the hybrid model using tuned crow search optimized dynamic graph neural network (TCSO-DGNN) on an automatically optimized process to enhance the accuracy of emotion detection. The proposed model results on emotion analysis data and superior results are obtained, indicating better performance in precision (94.72%), accuracy (96.43%), recall (94.36%), and F1-score (93.02%). The findings indicate that the inclusion of TCSO-DGNN significantly enhances the identification of complex emotional expressions concerning traditional NLP techniques. This study explores the potential of integrated DL approaches to refine the capabilities of emotion analysis tools, particularly in contexts requiring refined interpretations of human emotions expressed online. Additionally, it contributes to academic discourse in the field of emotion analysis while providing practical insights for developers of NLP applications across business and public service sectors.

Sentiment analysis is closely connected to Emotion Detection and Recognition from Text, which is a relatively recent area of research. Sentiment Analysis is a subset of Emotion Recognition that focuses on emotion extraction and analysis. Numerous research has been undertaken in the subject of Emotion Analysis since it is a growing trend to detect people’s feelings and sentiments in their day-to-day lives on a variety of personal and social concerns. Emotion Analysis attempts to recognise and perceive numerous emotions represented in texts, such as anger, contempt, fear, pleasure, sorrow, and surprise. People’s ability to accurately discern the emotions of others differs tremendously. Some of the feeling classifications include neutral, joy, sadness, love, hate, disgust, surprised, anger, fear, and so on. The data source was Twitter, a well-known social networking site. Text Mining or Natural Language Processing Algorithms are used to extract textual information for categorisation. Multiclass Emotions will be recognised in this proposed study utilising deep learning algorithms such as CNN, LSTM, and GRU using social media, specifically Twitter, as the data source, employing natural language processing methods, data augmentation, and feature extraction approaches.

The diversity of network language data forms, including text, images, audio and other modal data, is not only rich in information content, but also contains users' emotional attitudes and semantic intentions. This paper presents a model that incorporates the unique features of text, image, audio and other modal data. The model includes a multimodal emotion analysis module and a multimodal semantic understanding module. Deep learning method is used to analyze emotion and understand semantics, so as to improve the accuracy and robustness of natural language processing (NLP) technology. In the aspect of emotion analysis, the model combines the feature vectors of different modes by feature level fusion method, and uses the attention mechanism model based on Transformer to classify emotions. In the aspect of semantic understanding, the model integrates the deep features of text and image extracted by BERT and ResNet, and carries out cross-modal semantic reasoning through Transformer model. The experimental findings indicate that the multi-modal fusion model outperforms the single-modal model in emotion analysis, achieving an accuracy of 85.2%, 86.7% recall and 85.0% F1 value, which proves the potential of multi-modal data fusion in enhancing the performance of emotion analysis. At the same time, the precision of the model in semantic tag recognition is 79.3%, the recall rate is 76.8%, and the F1 value is 78.0%, which shows the robustness of the model in avoiding wrong classification and identifying related semantic information. This study provides more intelligent and accurate services for social media monitoring, customer feedback analysis, intelligent customer service and other application fields, and offers important insights for future studies in the area of multimodal information processing.

In recent years, emotional analysis has remained a hot topic, permeating people's daily lives and playing an indispensable role in various environments. Researchers can improve products and services and forecast user behavior using sentiment analysis to understand consumers' emotional inclinations and reactions. This paper's comprehensive review of emotional analysis is conducted through various research data. This study integrates emotional analysis and multimodal data research, elaborating on emotional analysis's characteristics and pros and cons from aspects such as facial expressions, voice information, and textual data. This paper points out the current applications of emotional analysis and recent research advancements, selecting various features and methods for emotion tendency classification, and utilizing existing technological tools for analysis. Lastly, this work combines multimodal data with various studies to make emotional analysis more widely and aptly applied in everyday life. There are greater opportunities for sentiment analysis in various domains thanks to the advancements in multimodal sentiment analysis research and modal fusion technologies.

Abstract. With the continuous improvement of many factors such as corpus, computing power, data scale, and laboratory conditions, how to quickly and accurately understand the emotional tendency in the text has become an important issue in many fields. The current state of affairs and the trajectory that deep learning is pursuing in the realm of text emotion analysis hold significant importance. This article first introduces the definition, importance and classification of emotional analysis, including the analysis of emotional polarity, emotional detection and fine-grained emotion. Subsequently, the key technologies of deep learning were discussed and the application of deep learning in natural language processing, especially LSTM, Bert, and their specific applications in emotional analysis within deep learning models. Then reveal the advantages and limitations of each model by comparing the performance of different deep learning models in emotional analysis tasks. Finally, in combination with current deep learning research, this study summarizes the challenges and research trends in the field of text emotional analysis.

Computer intelligent recognition technology refers to the use of computer vision, Natural Language Processing (NLP), machine learning and other technologies to enable computers to recognize, analyze, understand and answer human language and behavior. The common applications of computer intelligent recognition technology include image recognition, NLP, face recognition, target tracking, and other fields. NLP is a field of computer science, which involves the interaction between computers and natural languages. NLP technology can be used to process, analyze and generate natural language data, such as text, voice and image. Common NLP technology applications include language translation, emotion analysis, text classification, speech recognition and question answering system. Language model is a machine learning model, which uses a large number of text data for training to learn language patterns and relationships in text data. Although the language model has made great progress in the past few years, it still faces some challenges, including: poor semantic understanding, confusion in multilingual processing, slow language processing and other shortcomings. Therefore, in order to optimize these shortcomings, this article would study the pre-training language model based on NLP technology, which aimed at using NLP technology to optimize and improve the performance of the language model, thus optimizing the computer intelligent recognition technology. The model had a higher language understanding ability and more accurate prediction ability. In addition, the model could learn language rules and structures by using a large number of corpus, so as to better understand natural language. Through experiments, it could be known that the data size and total computing time of the traditional Generative Pretrained Transformer-2 (GPT-2) language model were 10 GB and 97 hours respectively. The data size and total computing time of BERT (Bidirectional Encoder Representations from Transformer) were 12 GB and 86 hours respectively. The data size and total computing time of the pre-training language model based on NLP were 18 GB and 71 hours respectively. Obviously, the pre-training language model based on NLP had a larger data size and shorter computing time. The experimental results showed that the NLP technology could better optimize the language model and effectively improve its various capabilities. This article opened up a new development direction for computer intelligent recognition technology and provided excellent technical support for the development of language models.

The development of natural language processing is significant for text emotion analysis because it helps to understand the expression of human emotions in different contexts and provides more accurate semantic understanding and emotion recognition capabilities for intelligent systems. In current natural language processing, sentiment analysis has become a key research field, and it is devoted to developing more accurate and efficient sentiment recognition models to adapt to the growing data scale and semantic complexity. This paper focuses on an overview of contemporary text emotion analysis technology and looks forward to the future development of natural language processing. This paper makes a detailed comparative analysis of the efficiency of different emotion analysis methods from the perspectives of key length, research content, research methods, and results. In the review, the advantages and limitations of various emotion analysis methods will be discussed in detail, including transformer-based and a series of the latest technologies. In addition, the performance differences of different methods of processing large-scale text data will be analyzed in-depth, and their performance in practical applications will be comprehensively evaluated. Finally, the research will discuss the possible future direction of natural language processing in emotion analysis in combination with current research trends and technology development trends to provide helpful enlightenment and guidance for researchers and practitioners in this field.

PurposeThe outbreak and continuation of COVID-19 have spawned the transformation of traditional teaching models to a certain extent. The Chinese Ministry of Education’s guidance on “keep learning and teaching during class suspension” has made OTC and learning (OTC) become routinized, and the public’s emotional attitudes toward OTC have also evolved over time. The purpose of this study is to segment the emotional text data and introduce it into the topic model to reveal the evolution process and stage characteristics of public emotional polarity and public opinion of OTC topics during public health emergencies in the context of social media participation. The research has important guiding significance for the development of OTC and can influence and improve the efficiency and effect of OTC to a certain extent. The analysis of online public opinion can provide suggestions for the government and media to guide the trend of public opinion and optimize the OTC model.Design/methodology/approachThis paper takes the topic of “OTC” on Zhihu during the COVID-19 epidemic as an example, combined with the characteristics of public opinion changes, chooses Boson emotional dictionary and time series analysis method to build an OTC network public opinion theme evolution analysis framework that integrates emotional analysis and topic mining. Finally, an empirical analysis of the dynamic evolution of the communication network for each stage of the life cycle of a specific topic is realized.FindingsThis paper draws the following conclusions: (1) Through the emotional value table and the change trend chart of the number of comments, the analysis found that the number of positive comments is greater than the number of negative comments, which can be inferred that the public gradually accepts “OTC” and presents a positive emotional state. (2) By observing the changing trend of the average daily emotional value of the public, it is found that the overall emotional value shows a stable development trend after a large fluctuation. From the actual emotional value and the fitted emotional value curve, it can be seen that the overall curve fit is good, so ARIMA (12, 1, 6) can accurately predict the dynamic trend of the daily average emotional value in this paper. Therefore, based on the above-mentioned public opinion, emotional analysis research, relevant countermeasures and suggestions are put forward, which is conducive to guiding the development direction of public opinion in a positive way.Originality/valueTaking the topic of “OTC” in Zhihu as an example, this paper combines Boson emotional dictionary and time series to conduct a series of research analyses. Boson emotional dictionary can analyze the public’s emotional tendency, and time series can well analyze the intrinsic structure and complex features of the data to predict the future values. The combination of the two research methods allows for an adequate and unique study of public emotional polarization and the evolution of public opinion.

Aim/Purpose. This paper addresses the challenge of emotional analysis in Hebrew texts, specifically focusing on enhancing machine learning techniques with psychological feature lexicons to improve classification accuracy in identifying depression. Background. Emotional analysis in Hebrew texts presents unique challenges due to the language's intricate morphology and rich derivation system. This paper seeks to leverage advanced machine learning methods augmented with carefully crafted psychological feature lexicons to address these challenges and improve the identification of depression from online discourse. Methodology. The study involves scraping and analyzing a dataset consisting of over 350K posts from 25K users on the "Camoni" health-related social network spanning 2010-2021. Various machine learning models, including SVM, Random Forest, Logistic Regression, and Multi-Layer Perceptron, were employed alongside ensemble methods such as Bagging, Boosting, and Stacking. Features were selected using TF-IDF, incorporating both word and character n-grams (Aisopos et al., 2016; HaCohen-Kerner et al., 2018). Pre-processing steps, including punctuation removal, stop word elimination, and lemmatization, were applied, to handle the challenges in Hebrew as a reach morphological language (Amram et al., 2018; Tsarfaty et al., 2019). Then hyperparameter tuning was conducted to optimize model performance across different languages. Following this, the models were enriched with features extracted from sentiment lexicons conducted by professional psychologists. (Shapira et al., 2021). Contribution. This paper contributes to the field by demonstrating the efficacy of integrating psychological feature lexicons into machine-learning models for emotional analysis in Hebrew texts. Addressing the unique linguistic challenges, it advances the understanding of depression detection in online communities and informs the development of more effective preventive measures and treatments. Findings. Through experimentation, it was discovered that enriching the models with features from sentiment lexicons significantly improved classification accuracy. Among the sentiment lexicons tested, six were identified as particularly enchanting: Negative emojis, positive emojis, neutral emojis, Hostile words, Anxiety words, and No-Trust words. The coverage and the quality of a feature lexicon are and may contribute to the success of various tasks like opinion mining and sentiment analysis (Feldman, 2013; Liu, 2012; Yang et al., 2020). Recommendations for Practitioners. Practitioners in mental health and social work should prioritize enriching machine learning models with sentiment lexicons to enhance the accuracy and effectiveness of depression detection in online discourse. By incorporating lexicons capturing emotional nuances, practitioners can improve the sensitivity of their screening processes. Recommendations for Researchers. Future research endeavors should focus on further refining machine learning models by enriching them with sentiment lexicons. Additionally, exploring the integration of sentiment lexicons into deep learning models could provide further insights into the classification of emotional content in textual data. Impact on Society. The findings have significant implications for the development of more accurate and efficient methods for detecting depression in online Hebrew discourse. By leveraging advanced machine learning techniques augmented with psychological feature lexicons, this research contributes to enhancing mental health interventions and promoting well-being in online communities. Future Research. Future research should not only continue exploring the integration of sentiment lexicons into machine learning models but also extend this investigation to deep learning architectures. Investigating the effectiveness of sentiment lexicons in enhancing the performance of deep learning models could advance our understanding of emotional analysis in textual data and improve depression detection algorithms.

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

Humans are using online social networks to share their opinions and thoughts on a variety of subjects and topics with their friends, family, and relations through text, photographs, audio and video messages and posts. On specific social, national, and global topics, humans can share their thoughts, mental states, moments, and viewpoints. Given the variety of communication options available, text is one of the most popular mediums of communication on social media. The study described here aims to detect and analyses sentiment and emotion expressed by people in their messages, and then use that information to generate suggestions. Humans collected comments and replies on a few specific topics and created a dataset with text, sentiment emotion, and other data. Emotion identification from Text is a new topic of research that is closely related to sentiment analysis. Anger, disgust, fear, happiness, sadness, and surprise are examples of emotions that may be detected and understood by the expression of texts using Emotion Analysis. Emotion Detection focuses on feature extraction and word recognition because pre-processing techniques improve accuracy of classification.Humans are using online social networks to share their opinions and thoughts on a variety of subjects and topics with their friends, family, and relations through text, photographs, audio and video messages and posts. On specific social, national, and global topics, humans can share their thoughts, mental states, moments, and viewpoints. Given the variety of communication options available, text is one of the most popular mediums of communication on social media. The study described here aims to detect and analyses sentiment and emotion expressed by people in their messages, and then use that information to generate suggestions. Humans collected comments and replies on a few specific topics and created a dataset with text, sentiment emotion, and other data. Emotion identification from Text is a new topic of research that is closely related to sentiment analysis. Anger, disgust, fear, happiness, sadness, and surprise are examples of emotions that may be detected and understood by the expression of texts using Emotion Analysis. Emotion Detection focuses on feature extraction and word recognition because pre-processing techniques improve accuracy of classification.Humans are using online social networks to share their opinions and thoughts on a variety of subjects and topics with their friends, family, and relations through text, photographs, audio and video messages and posts. On specific social, national, and global topics, humans can share their thoughts, mental states, moments, and viewpoints. Given the variety of communication options available, text is one of the most popular mediums of communication on social media. The study described here aims to detect and analyses sentiment and emotion expressed by people in their messages, and then use that information to generate suggestions. Humans collected comments and replies on a few specific topics and created a dataset with text, sentiment emotion, and other data. Emotion identification from Text is a new topic of research that is closely related to sentiment analysis. Anger, disgust, fear, happiness, sadness, and surprise are examples of emotions that may be detected and understood by the expression of texts using Emotion Analysis. Emotion Detection focuses on feature extraction and word recognition because pre-processing techniques improve accuracy of classification.

Natural language processing and machine learning are used to define and extract human emotions from unstructured text using a technique called sentiment analysis. Many organizations and companies today want to use this to recognize and act accordingly on the customer or user's features. This increases the importance and effectiveness of emotion analysis and the diversity of algorithms used day by day. One of these algorithms is the Extreme Learning machine. The Extreme Learning machine (ELM) algorithm is an important machine learning algorithm for emotion analysis and classification. In this study, the method used in the ELM's emotional analysis is systematic research that shows that the context and its applications have been studied. A systematic review of the works published between 2020 and 2022 was carried out using Web of Science and Google Scholar databases. After the first and in-depth screening of the literature, 10 of the 28 articles were selected from the review process. The articles have been reviewed based on the purpose of the study and research questions. According to the research results, different methods were used in the emotional analysis, mostly with the ELM, and ELM’s performance was improved. Quality analysis of treatment summaries is used in different areas, such as health care, education, and website product assessments. ELM's use of emotion analysis has resulted in most social media data as a scope, especially the Twitter platform.