Evolution and spatiotemporal analysis of earthquake public opinion based on social media data

As critical conduits for the dissemination of online public opinion,social media platforms offer a timely and effective means for managing emergencies during major disasters,such as earthquakes.This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province.By collecting,cleaning,and organizing post-earthquake Sina Weibo(short for Weibo)data,we employed the Latent Dirichlet Allocation(LDA)model to extract information pertinent to public opinion on these earthquakes.This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events.An emotion analysis,utilizing an emotion dictionary,categorized the emotional content of post-earthquake Weibo posts,facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes.The findings were visualized using Geographic Information System(GIS)techniques.The analysis revealed certain commonalities in online public opinion following both earthquakes.Notably,the peak of online engagement occurred within the first 24 hours post-earthquake,with a rapid decline observed between 24 to 48 hours thereafter.The variation in popularity of online public opinion was linked to aftershock occurrences.Adjusted for population factors,online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high.Initially dominated by feelings of“fear”and“surprise”,the public sentiment shifted towards a more positive outlook with the onset of rescue operations.However,distinctions in the online public response to each earthquake were also noted.Following the Yangbi earthquake,Yunnan Province reported the highest number of Weibo posts nationwide;in contrast,Qinghai Province ranked third post-Maduo earthquake,attributable to its smaller population size and extensive damage to communication infrastructure.This research offers a methodological approach for the analysis of online public opinion related to earthquakes,providing insights for the enhancement of post-disaster emergency management and public mental health support.

Public Sentiment Analysis of Social Security Emergencies Based on Feature Fusion Model of BERT and TextLevelGCN

At present, the emotion classification method of Weibo public opinions based on graph neural network cannot solve the polysemy problem well, and the scale of global graph with fixed weight is too large. This paper proposes a feature fusion network model Bert-TextLevelGCN based on BERT pre-training and improved TextGCN. On the one hand, Bert is introduced to obtain the initial vector input of graph neural network containing rich semantic features. On the other hand, the global graph connection window of traditional TextGCN is reduced to the text level, and the message propagation mechanism of global sharing is applied. Finally, the output vector of BERT and TextLevelGCN is fused by interpolation update method, and a more robust mapping of positive and negative sentiment classification of public opinion text of “Tangshan Barbecue Restaurant beating people” is obtained. In the context of the national anti-gang campaign, it is of great significance to accurately and efficiently analyze the emotional characteristics of public opinion in sudden social violence events with bad social impact, which is of great significance to improve the government’s public opinion warning and response ability to public opinion in sudden social security events. .

Multimodal sentiment analysis for social media contents during public emergencies

Purpose:Nowadays,public opinions during public emergencies involve not only textual contents but also contain images.However,the existing works mainly focus on textual contents and they do not provide a satisfactory accuracy of sentiment analysis,lacking the combination of multimodal contents.In this paper,we propose to combine texts and images generated in the social media to perform sentiment analysis.Design/methodology/approach:We propose a Deep Multimodal Fusion Model(DMFM),which combines textual and visual sentiment analysis.We first train word2vec model on a large-scale public emergency corpus to obtain semantic-rich word vectors as the input of textual sentiment analysis.BiLSTM is employed to generate encoded textual embeddings.To fully excavate visual information from images,a modified pretrained VGG16-based sentiment analysis network is used with the best-performed fine-tuning strategy.A multimodal fusion method is implemented to fuse textual and visual embeddings completely,producing predicted labels.Findings:We performed extensive experiments on Weibo and Twitter public emergency datasets,to evaluate the performance of our proposed model.Experimental results demonstrate that the DMFM provides higher accuracy compared with baseline models.The introduction of images can boost the performance of sentiment analysis during public emergencies.Research limitations:In the future,we will test our model in a wider dataset.We will also consider a better way to learn the multimodal fusion information.Practical implications:We build an efficient multimodal sentiment analysis model for the social media contents during public emergencies.Originality/value:We consider the images posted by online users during public emergencies on social platforms.The proposed method can present a novel scope for sentiment analysis during public emergencies and provide the decision support for the government when formulating policies in public emergencies.

Research on Prediction of Sentiment Trend of Food Safety Public Opinion

Emotion has a nearly decisive role in behavior, which will directly affect netizens’ views on food safety public opinion events, thereby affecting the development direction of public opinion on the event, and it is of great significance for food safety network public opinion to predict emotional trends to do a good job in food safety network public opinion guidance. In this paper, the dynamic text representation method XLNet is used to generate word vectors with context-dependent dependencies to distribute the text information of food safety network public opinion. Then, the word vector is input into the CNN-BiLSTM network for local semantic feature and context semantic extraction. The attention mechanism is introduced to give different weights according to the importance of features, and the emotional tendency analysis is carried out. Based on sentiment analysis, sentiment value time series data is obtained, and a time series model is constructed to predict sentiment trends. The sentiment analysis model proposed in this paper can well classify the sentiment of food safety network public opinion, and the time series model has a good effect on the prediction of food safety network public opinion sentiment trend. .

Design and Implementation of Weibo Sentiment Analysis Based on LDA and Dependency Parsing

Information content security is a branch of cyberspace security. How to effectively manage and use Weibo comment information has become a research focus in the field of information content security. Three main tasks involved are emotion sentence identification and classification,emotion tendency classification,and emotion expression extraction. Combining with the latent Dirichlet allocation(LDA) model,a Gibbs sampling implementation for inference of our algorithm is presented,and can be used to categorize emotion tendency automatically with the computer. In accordance with the lower ratio of recall for emotion expression extraction in Weibo,use dependency parsing,divided into two categories with subject and object,summarized six kinds of dependency models from evaluating objects and emotion words,and proposed that a merge algorithm for evaluating objects can be accurately evaluated by participating in a public bakeoff and in the shared tasks among the best methods in the sub-task of emotion expression extraction,indicating the value of our method as not only innovative but practical.

Emotional Vector Distance Based Sentiment Analysis of Wakamono Kotoba

In this paper,we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system,by using Wakamono Kotoba example sentences as features.The proposed method applies Earth Mover's Distance(EMD) to vector of words.As a result of the evaluation experiment using 14 440 sentences,higher estimation accuracy is obtained by considering emotional distance between words-an approach that had not been used in the conventional research-than by using only word importance value.

Public Opinions on ChatGPT:An Analysis of Reddit Discussions by Using Sentiment Analysis,Topic Modeling,and SWOT Analysis

The sudden arrival of AI(Artificial Intelligence) into people's daily lives all around the world was marked by the introduction of ChatGPT, which was officially released on November 30, 2022. This AI invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields, as its capacity extends across various fields. Consequently, numerous articles and journals have been discussing ChatGPT, making it a headline for several topics. However, it does not reflect most public opinion about the product. Therefore, this paper investigated the public's opinions on ChatGPT through topic modelling, Vader-based sentiment analysis and SWOT analysis. To gather data for this study, 202905 comments from the Reddit platform were collected between December 2022 and December 2023. The findings reveal that the Reddit community engaged in discussions related to ChatGPT, covering a range of topics including comparisons with traditional search engines, the impacts on software development, job market, and education industry, exploring ChatGPT's responses on entertainment and politics, the responses from Dan, the alter ego of ChatGPT, the ethical usage of user data as well as queries related to the AI-generated images. The sentiment analysis indicates that most people hold positive views towards this innovative technology across these several aspects. However, concerns also arise regarding the potential negative impacts associated with this product. The SWOT analysis of these results highlights both the strengths and pain points, market opportunities and threats associated with ChatGPT. This analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.

COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining

With the spread and development of new epidemics,it is of great reference value to identify the changing trends of epidemics in public emotions.We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment are proposed.Establish a“Scrapy-Redis-Bloomfilter”distributed crawler framework to collect data.The system can judge the positive and negative emotions of the reviewer based on the comments,and can also reflect the depth of the seven emotions such as Hopeful,Happy,and Depressed.Finally,we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model.The results show that our model has better generalization ability and smaller discriminant error.We designed a large data visualization screen,which can clearly show the trend of public emotions,the proportion of various emotion categories,keywords,hot topics,etc.,and fully and intuitively reflect the development of public opinion.

An Unsupervised Method for Short-Text Sentiment Analysis Based on Analysis of Massive Data

Common forms of short text are microblogs, Twitter posts, short product reviews, short movie reviews and instant messages. Sentiment analysis of them has been a hot topic. A highly-accurate model is proposed in this paper for short-text sentiment analysis. The researches target microblog, product review and movie reviews. Words, symbols or sentences with emotional tendencies are proved important indicators in short-text sentiment analysis based on massive users’ data. It is an effective method to predict emotional tendencies of short text using these features. The model has noticed the phenomenon of polysemy in single-character emotional word in Chinese and discusses singlecharacter and multi-character emotional word separately. The idea of model can be used to deal with various kinds of short-text data. Experiments show that this model performs well in most cases.

A survey on deep learning for textual emotion analysis in social networks

Textual Emotion Analysis(TEA)aims to extract and analyze user emotional states in texts.Various Deep Learning(DL)methods have developed rapidly,and they have proven to be successful in many fields such as audio,image,and natural language processing.This trend has drawn increasing researchers away from traditional machine learning to DL for their scientific research.In this paper,we provide an overview of TEA based on DL methods.After introducing a background for emotion analysis that includes defining emotion,emotion classification methods,and application domains of emotion analysis,we summarize DL technology,and the word/sentence representation learning method.We then categorize existing TEA methods based on text structures and linguistic types:text-oriented monolingual methods,text conversations-oriented monolingual methods,text-oriented cross-linguistic methods,and emoji-oriented cross-linguistic methods.We close by discussing emotion analysis challenges and future research trends.We hope that our survey will assist readers in understanding the relationship between TEA and DL methods while also improving TEA development.

Public Reaction to Scientific Research via Twitter Sentiment Prediction

Purpose:Social media users share their ideas,thoughts,and emotions with other users.However,it is not clear how online users would respond to new re search outcomes.This study aims to predict the nature of the emotions expressed by Twitter users toward scientific publications.Additionally,we investigate what features of the research articles help in such prediction.Identifying the sentiments of research articles on social media will help scientists gauge a new societal impact of their research articles.Design/methodology/appro ach:Several tools are used for sentiment analysis,so we applied five sentiment analysis tools to check which are suitable for capturing a tweet’s sentiment value and decided to use NLTK VADER and TextBlob.We segregated the sentiment value into negative,positive,and neutral.We measure the mean and median of tweets’sentiment value for research articles with more than one tweet.We next built machine learning models to predict the sentiments of tweets related to scientific publications and investigated the essential features that controlled the prediction models.Findings:We found that the most important feature in all the models was the sentiment of the research article title followed by the author count.We observed that the tree-based models performed better than other classification models,with Random Forest achieving 89%accuracy for binary clas sification and 73%accuracy for three-label clas sification.Research limitations:In this research,we used state-of-the-art sentiment analysis libraries.However,these libraries might vary at times in their sentiment prediction behavior.Tweet sentiment may be influenced by a multitude of circumstances and is not always immediately tied to the paper’s details.In the future,we intend to broaden the scope of our research by employing word2 vec models.Practical implications:Many studies have focused on understanding the impact of science on scientists or how science communicators can improve their outcomes.Research in this area has relied on fewer and more limited measures,such as citations and user studies with small datasets.There is currently a critical need to find novel methods to quantify and evaluate the broader impact of research.This study will help scientists better comprehend the emotional impact of their work.Additionally,the value of understanding the public’s interest and reactions helps science communicators identify effective ways to engage with the public and build positive connections between scientific communities and the public.Originality/value:This study will extend work on public engagement with science,sociology of science,and computational social science.It will enable researchers to identify areas in which there is a gap between public and expert understanding and provide strategies by which this gap can be bridged.

The Application of R Software in Text Analysis

With the development of Web 2.0,more and more people choose to use the Internet to express their opinions.All this opinions together into a new form text which contains a lot of valuable emotional information,this is why how to deal with these texts and analysis the emotional information is significant for us.We get three main tasks of sentiment analysis,including sentiment extraction,sentiment classification,sentiment application and summarization.In this paper,based on the R software,we introduced the steps of sentiment analysis in detail.Finally,we collect the movie reviews from the Internet,and use R software to do sentiment analysis in order to judge the emotional tendency of the text.

The Impact of BW Emotional Index on China's A - Share Market Returns

Investor sentiment has its characteristics of the inherent complexity and changing. In this paper, through the analysis of the "investor sentiment and cross-sectional data on the impact of stock returns"[1] published by Baker and others in 2006, combining with the specific situation of China's securities market, and basing on the BW model to select Shanghai and Shenzhen 300 index turnover rate and other 5 emotional indices, and using the principal component analysis to build a monthly investor sentiment index of China's securities market. The principal component analysis of residual that calculated by a regression of emotional indicators and macroeconomic data is carried out, to get macroscopical emotional indicators that removes macro factors. Finally, OLS regression analysis is carried out with the Shanghai Composite Index and Shenzhen Component Index to find that the constructed emotional index has a significant effect on the yield of China's stock market, thus verifying the validity of the emotional index.

Predicting and Curing Depression Using Long Short Term Memory and Global Vector

In today’s world, there are many people suffering from mentalhealth problems such as depression and anxiety. If these conditions are notidentified and treated early, they can get worse quickly and have far-reachingnegative effects. Unfortunately, many people suffering from these conditions,especially depression and hypertension, are unaware of their existence until theconditions become chronic. Thus, this paper proposes a novel approach usingBi-directional Long Short-Term Memory (Bi-LSTM) algorithm and GlobalVector (GloVe) algorithm for the prediction and treatment of these conditions.Smartwatches and fitness bands can be equipped with these algorithms whichcan share data with a variety of IoT devices and smart systems to betterunderstand and analyze the user’s condition. We compared the accuracy andloss of the training dataset and the validation dataset of the two modelsnamely, Bi-LSTM without a global vector layer and with a global vector layer.It was observed that the model of Bi-LSTM without a global vector layer hadan accuracy of 83%,while Bi-LSTMwith a global vector layer had an accuracyof 86% with a precision of 86.4%, and an F1 score of 0.861. In addition toproviding basic therapies for the treatment of identified cases, our model alsohelps prevent the deterioration of associated conditions, making our methoda real-world solution.

Deer Hunting Optimization with Deep Learning Enabled Emotion Classification on English Twitter Data

Currently,individuals use online social media,namely Facebook or Twitter,for sharing their thoughts and emotions.Detection of emotions on social networking sites’finds useful in several applications in social welfare,commerce,public health,and so on.Emotion is expressed in several means,like facial and speech expressions,gestures,and written text.Emotion recognition in a text document is a content-based classification problem that includes notions from deep learning(DL)and natural language processing(NLP)domains.This article proposes a Deer HuntingOptimizationwithDeep Belief Network Enabled Emotion Classification(DHODBN-EC)on English Twitter Data in this study.The presented DHODBN-EC model aims to examine the existence of distinct emotion classes in tweets.At the introductory level,the DHODBN-EC technique pre-processes the tweets at different levels.Besides,the word2vec feature extraction process is applied to generate the word embedding process.For emotion classification,the DHODBN-EC model utilizes the DBN model,which helps to determine distinct emotion class labels.Lastly,the DHO algorithm is leveraged for optimal hyperparameter adjustment of the DBN technique.An extensive range of experimental analyses can be executed to demonstrate the enhanced performance of the DHODBN-EC approach.A comprehensive comparison study exhibited the improvements of the DHODBN-EC model over other approaches with increased accuracy of 96.67%.

Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe.However,there are fewer studies concerning Sentiment Analysis(SA)in Arabic.In recent years,the detected sentiments and emotions expressed in tweets have received significant interest.The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language.Two common models are available:Machine Learning and lexicon-based approaches to address emotion classification problems.With this motivation,the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification(TLBOML-ERC)model for Sentiment Analysis on tweets made in the Arabic language.The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets.To attain this,the proposed TLBOMLERC model initially carries out data pre-processing and a Continuous Bag Of Words(CBOW)-based word embedding process.In addition,Denoising Autoencoder(DAE)model is also exploited to categorise different emotions expressed in Arabic tweets.To improve the efficacy of the DAE model,the Teaching and Learning-based Optimization(TLBO)algorithm is utilized to optimize the parameters.The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset.The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.

Emotion Deduction from Social Media Text Data Using Machine Learning Algorithm

Emotion represents the feeling of an individual in a given situation. There are various ways to express the emotions of an individual. It can be categorized into verbal expressions, written expressions, facial expressions and gestures. Among these various ways of expressing the emotion, the written method is a challenging task to extract the emotions, as the data is in the form of textual dat. Finding the different kinds of emotions is also a tedious task as it requires a lot of pre preparations of the textual data taken for the research. This research work is carried out to analyse and extract the emotions hidden in text data. The text data taken for the analysis is from the social media dataset. Using the raw text data directly from the social media will not serve the purpose. Therefore, the text data has to be pre-processed and then utilised for further processing. Pre-processing makes the text data more efficient and would infer valuable insights of the emotions hidden in it. The preprocessing steps also help to manage the text data for identifying the emotions conveyed in the text. This work proposes to deduct the emotions taken from the social media text data by applying the machine learning algorithm. Finally, the usefulness of the emotions is suggested for various stake holders, to find the attitude of individuals at that moment, the data is produced. .

情感分歧对社交媒体中信息再传播的影响——以微博为例

[研究目的]社交媒体中信息的再传播是目前主流传播方式之一,研究情感特征对社交媒体中信息再传播的作用,揭示情感信息影响社交媒体中信息再传播的机制,对于科学引导网络舆论、维持健康网络环境具有重要意义。[研究方法]基于认知失调理论,引入情感分歧特征进行情感分析,以社交媒体中帖子的转发数与评论数作为信息再传播效果的衡量指标,构建了情感分歧与情感倾向影响信息再传播的理论模型,并使用微博平台上的数据,对模型进行验证。[研究结论]通过对回归模型结果分析,情感分歧能够显著正向影响社交媒体信息再传播,促进帖子的转发与评论;正面情感倾向性能够显著促进帖子的转发效果,对帖子的评论数影响效果并不显著;帖子内容的情感倾向性能够调节帖子内容中的情感分歧对帖子转发的影响,而评论情感倾向能够调节评论内容中的情感分歧对帖子评论数的影响。

基于情感分析的网络舆情共振研究

为有效应对复杂多变的舆情环境,研究孤立发生的单一舆情事件演化成具有某些相同特征的多舆情事件簇或事件集,针对同议题、同情绪等多起舆情事件建立网络舆情共振模型,通过爬取“唐山打人案”和“唐山打人事件被害人首次发声”事件相关微博数据,以及“2021年河南遭遇特大暴雨”和“2023年河北暴雨”相关微博数据,将评论数据进行BosonNLP情感分析,得出其情感分数;并将情感分数作为模型参数,分别对2起不同类型的案例进行检验。研究结果表明:在原生舆情与次生舆情共同作用下,引起网民情绪感染和矛盾冲突,从而发生网络舆情共振,并且共振产生的热度高于单一事件热度;网民消极的态度值会加速舆情共振、不同类型的事件所产生的舆情共振效果是不同的。研究结果可丰富网络舆情以及社会物理学相关理论,可为构建舆情共振的研究框架提供参考。