Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

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. .

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.

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。

ChatGPT网络舆情特征多维度演化分析

[研究目的]ChatGPT在世界范围内刷爆网络,引起全球的关注和热议。互联网上针对ChatGPT产生多种不同的声音,从广泛社会舆论视角探究ChatGPT网络舆情特征的多维度演化规律,有利于为新兴科技类产品的舆情研判和利益相关者决策提供支撑。[研究方法]基于传播学视角构建了ChatGPT网络舆情特征多维度演化分析框架,以微博为数据获取平台,使用意见领袖影响力、LDA主题挖掘以及SnowNLP情感分析方法对ChatGPT网络舆情多维度特征演化展开分析。[研究结论]ChatGPT网络舆情发展分为缓慢萌发期、火爆出圈期、波动下降期以及稳定发展期四个阶段;初期意见领袖中包含科技科普博主、权威媒体以及社会公众人物,后期自媒体和名人的意见领袖作用逐渐被权威媒体和网络平台媒体所取代;热点主题数量增加,且主题不断细化和深化。公众对ChatGPT的情感以积极乐观为主。ChatGPT网络舆情的情感倾向与主题演变趋势相一致。

地方公费师范生乡村教育情怀现状、问题与对策

地方公费师范生乡村教育情怀整体水平中等偏高,且显著优于普通师范生,但随年级升高呈下降趋势,少数公费师范生乡村教育情怀不足,城市学生相对偏低。为厚植公费师范生乡村教育情怀,应进一步完善公费师范生遴选和退出机制,增设乡村教育特色课程,培养乡村教研人才,强化见习实习指导,重视过程培养,使公费师范生对乡村教育未来充满希望。

基于情感分析和热度预测的网络舆情预测研究

在社交媒体平台已成为大众信息交流的重要载体的背景下,关注和分析大众对于社会热点事件的情感倾向及舆论走势,有利于及时准确了解大众的情感需求,制定相应的措施,引导舆论走向,维护良好的网络环境。提出了基于情感分析和热度预测的网络舆情预测思路,构建了融合多特征的文本情感分析模型和基于时间序列的热度预测模型,并基于真实数据集验证了模型的有效性。对于社交媒体上舆论环境的分析和预测有重要意义。

分布式光伏开发公众评论的情感倾向及引导策略

整县推进屋顶分布式光伏开发试点工作的实施,推动了分布式光伏的快速发展,也引发了公众在社交媒体上的热烈讨论,其中不乏有大量的负面评论。这些评论一方面映射了分布式光伏开发实际操作中存在的乱象和问题,另一方面也使公众对分布式光伏开发产生了极大的误解和偏见,进而阻碍了分布式光伏开发的进程。当前对分布式光伏开发公众认知和采纳意愿的研究以问卷调查为主,缺乏基于评论数据的研究。以整县推进屋顶分布式光伏开发为背景,基于抖音评论数据,利用情感分析与BERTopic主题建模方法,探讨了公众对分布式光伏开发的情感倾向及主题特征。研究结果表明,公众评论以负面为主,在时间上波动较大,且与媒体负面报道有较大的关联性,评论数量在空间上呈集中分布态势,与试点县域数量、分布式光伏开发进度密切相关;公众正面情绪主要源于国家推广力度及政府监管强度,负面情绪主要源于公众对“光伏骗局”的担忧。结合以上结果,从政府监管、舆论引导、后期保障三个方面提出了屋顶分布式光伏发展的政策建议。

基于Kano模型的突发公共事件政务微博回应与公众信息需求适配性研究

【目的/意义】政府回应是政府应对突发事件时维稳民心、疏导舆论的重要举措。对公众信息需求进行精准定位并对其与政务微博回应作适配性分析,有助于总结政府回应的经验教训并明确改进策略,为提高回应效能提供参考。【方法/过程】基于公众在线评论数据,采用Kano模型对公众信息需求进行具体的属性定调,分析舆情演化不同阶段下公众信息需求与政府回应适配性的差异化表征,提出提升政府回应效能的对策建议。【结果/结论】突发事件发生后公众的信息需求呈现差异化表征,公众信息需求与政府回应的适配度会直接影响回应的精准度与公众的满意度。因此,在应对突发公共舆情事件时,政府应根据公众的情感与真实需求精准调整、优化和改进回应策略,树立良好的政府形象。

重大突发事件下网络舆情传播中回声室网络结构研究

[目的/意义]在社交网络中回声室对重大突发事件下的社交网络舆情演进具有重要作用,研究重大突发事件网络舆情中回声室网络结构可以为相关部门的舆情引导和管控提供指导和帮助。[方法/过程]基于网络结构理论,利用社会网络分析法和情感分析方法,进行用户回声室网络结构识别以及用户回声室网络结构表征及网络同质性检验。在此基础上,构建重大突发事件中回声室网络结构分析模型,并结合重大突发事件“3·21”东航客机事故中典型舆情话题进行实证研究。[结果/结论]重大突发事件网络舆情中存在回声室效应,由于微博评论与转发机制的不同,回声室网络结构存在明显的网络特征差异,且评论机制更有利于社交网络在重大突发事件下的情感分享,并可以凝聚群体观点和用户想法,从而促进意见领袖在群内和群外成员之间的互动。

基于知乎平台内容挖掘的元宇宙公众感知研究

[目的/意义]通过分析公众对元宇宙中个体、业界、政府3方面内容的感知,针对性地为个体、企业、政府参与元宇宙生态建设提供建议。[方法/过程]参考三螺旋模型,构建基于个体—业界—政府维度的元宇宙公众感知分析框架,采用改进LDA和BERT算法,基于知乎问答分析公众对元宇宙中个体、业界、政府3方面内容的感知主题,主题的热度、情感及相关演化。[结果/结论]公众在个体、业界、政府维度上的感知既有差异又有联系。演化上,公众关注点逐渐细化深入,态度受体验、舆论影响显著,但趋于理性。通过挖掘不同维度的热点、潜力点及问题,能针对性地为不同主体提供建议。

高职农业院校公共课培育“三农”情怀的路径探析--以“信息技术基础”课程为例

通过对学生“离农”现象的现实审视,从国家、学校和课程三个层面阐述高职农业院校在公共课中培育“三农”情怀的内在逻辑。基于“思政引领、三全育人”的实践思路,聚焦农业农村现代化需求,明确教学目标;以“三农”案例为载体,“课标、能力、思想”三线融合,重构课程内容;“育心、练技、立志、践行”四路并进,创新教学方法;开展多样化评价,落实培养效果,探索高职农业院校公共课培育“三农”情怀的新路径。

基于文本挖掘的图书馆舆情情感分析

随着移动互联网在高校图书馆年轻读者的影响越来越大,新生代读者使用QQ即时通信软件的比例越来越大,对于图书馆QQ群文本信息进行文本挖掘从而了解图书馆的舆情,可以用于图书馆舆情预警,为图书馆决策层提供更强的舆情应对能力。采用网络爬虫技术爬取2022年9月至2022年12月份时间段内的QQ群中聊天记录作为图书馆舆情数据,接着对原始的舆情数据进行数据去重、清洗等数据预处理操作,然后运用清华大学Thulac分词技术提取舆情数据的关键字、计算其权重,并使用WordCloud库进行可视化展示,接下来采用spaCy库给舆情数据计算出具体的情感倾向及分值,最后通过实验来分析图书馆舆情的情感倾向。

基于食品添加剂引发食品安全网络舆情协同治理研究

食品安全历来受到广泛关注,近年来因食品添加剂使用不当、非法使用添加物等而引发食品安全事件,已成为公众情绪的“催化剂”,并迅速演化为网络舆情,影响企业自身发展和社会安定稳定。以海天酱油“双标”事件为例,结合舆情传播生命周期模型,通过问卷调查、内容分析法等,梳理食品安全网络舆情演化的基本规律,进一步探析食品添加剂引发食品安全网络舆情的主体影响因素,并提出建构多元协同治理体系及具体路径。

基于震后舆情的灾情信息提取研究

大地震发生后,通常会出现大量关于地震灾害的信息并在网络中快速传播,为快速准确地获取地震灾情信息,开展基于震后舆情提取灾情信息的相关研究。首先构建灾情信息挖掘模型,建立灾情关键词表,通过计算词向量相似度,快速提取地震灾情等相关信息。同时以“山东德州原平5.5级地震”为案例,验证构建模型在地震灾情提取方面的效果及其可行性和实用性。结果表明,该研究可为震后快速应对和处置灾情提供一定的参考数据和决策依据。

对舆情精准研判的学理思考

在当今时代,存在着各类舆情勃发的土壤和条件。以公众心声为核心内容的舆情,内含政治诉求、经济诉求、文化诉求和心理诉求等,以自己的方式聚集、形成、传播、运行、起落。舆情研判是特定主体在对舆情所作的研究分析的基础上所作的判断。舆情精准研判须以正确认知和深刻理解舆情为起点、为基础。舆情和舆论联系紧密但并不能等同。精准研判舆情有赖于走出对舆情的认知误区。舆情是研判对象,研究、分析呈现为持续的过程,判断是舆情研判形成的结果。进行精准舆情研判须符合基本程序,准确把握舆情精准研判的精髓,实现“研”与“判”的有机结合,就动态发展的舆情进行研判。舆情精准研判的着重点是:关注引发舆情的种种情形,进而关注影响舆情起伏的各种重要的作用力,按舆情运行规律进行舆情研判。舆情精准研判的关键点有:抓好对始发点、聚焦点、转折点、引爆点舆情的研判。

一种基于立场阵营判定的评论信息情感分类方法

舆论热点事件发生时,网友会在社交媒体平台发表自己的观点和评论,为准确感知舆论场网民观点态势,针对网络社交媒体平台上网友对时事政治、重大热点事件等话题的评论信息,提出了一种基于立场阵营判定的评论信息情感分类方法。首先对评论信息涉及的评价对象立场阵营进行细分,在此基础上,构建情感分类模型,实现评论信息情感倾向的自动分类,达到准确感知舆论场网民的情感倾向的目的,从而为网络空间舆论风险评估、高危话题研判提供支撑。

社交网络舆情多模态知识图谱构建框架研究

[研究目的]信息技术的发展丰富了社交媒体用户的沟通交流方式,研究社交网络舆情多模态知识图谱的构建对网络舆情治理具有重要的现实意义。[研究方法]基于多模态数据的语义互补,讨论了实体属性关联、图像(视频)文字描述、图像(视频)属性、图像(视频)关联等多种异构数据融合方式。在此基础上,提出社交网络舆情多模态知识图谱的构建框架,并分析了在多模态语义理解、多模态实体对齐、多模态知识表示等方面存在的问题与挑战。[研究结论]提出基于多模态知识融合的社交网络舆情多模态知识图谱构建框架,为交互方式日趋丰富的社交网络舆情治理提供有益参考。

网络舆情视域下大学生网络素养提升探究

随着互联网信息技术的日益发展,高校网络舆情管理不断接受新的挑战。高校网络舆情是高校师生通过互联网对校园生活中的某些现象、校园管理以及其所关注的其他社会现象、社会问题所表达的情绪、意愿、态度和意见的总和。舆情事件类型通常包括校园管理、师德师风、家校关系、教育改革等与学生息息相关的方面。通过梳理总结高校网络舆情视角下大学生主体特征,分析高校网络舆情与大学生网络素养建设之间的辩证关系,探究增强大学生网络素养建设的相关对策,引导高校网络舆情正向发展。