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.

The Indicator System Based on BP Neural Network Model for Net-mediated Public Opinion on Unexpected Emergency

Because unexpected emergency owns the characteristics of explosive,uncertain evolution direction and group diffusion,more and more researchers concentrate on and try to control it. In addition,considering the force of network,the information of the unexpected emergency will be spread and enlarged rapidly on internet. It is a new viewpoint using the indicator system to estimate the heat degree of net-mediated public opinion on unexpected emergency,which can reveal the underlying reasons about the formation of the heat degree. Moreover,we use BP(Back Propagation) neural network method instead of traditional subjective weight assignment to calculate the weights of the indicators which can make evaluation results more accurate and objective.

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。

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。

突发公共卫生事件对消费者在线评论情感倾向的影响研究

基于情感信息理论,以去哪儿网北京市酒店信息和评论数据为样本,探讨突发公共卫生事件对消费者评论情感倾向的影响,以及消费者洞察力水平与确定性水平的调节作用。研究表明,突发公共卫生事件降低了消费者评论的积极情感倾向,并提升了其消极情感倾向;消费者的洞察力水平与确定性水平会削弱突发公共卫生事件对消费者评论积极情感倾向的负向影响,但未显著调节消费者评论消极情感倾向所受影响。运用主题情感分析方法,对消费者评论情感倾向在不同时期内分布的异同进一步比较分析发现,突发公共卫生事件发生后,消费者对酒店体验方面的积极感知提升,而对酒店硬件设施方面的不满加剧。

聚焦热度变化、主题动态与情感趋势的微博舆情演化研究

[研究目的]系统探讨微博舆情事件的演化特征,以提出针对性的对策建议,避免网络舆情扩散所可能引发的不利影响。[研究方法]为实现该目的,提出了基于CNN-BiLSTM-Attention的微博舆情多维特征演化分析框架,以深入剖析网络舆情的形成机制,进而优化对网络舆情的应对和处理策略。[研究结论]根据选取的事件从新浪微博获取数据,基于TF-IDF模型和K-Means聚类算法对微博舆情事件进行了维度划分,通过组合模型CNN-BiLSTM-Attention进行情感分类,并验证其准确性。最后,根据维度划分和情感分类的结果,结合舆情生命周期理论,从舆情热度、主题和情感三个方面研究了微博舆情事件的演化情况,并从生命周期和主题情感两方面得出网络舆情应对策略。

乡村文化传承与空间重构的逻辑辨析和重构路径

基于场域理论,从乡村文化场域的物质要素和非物质要素两方面研究建构乡村文化传承与空间重构的逻辑框架,探索在乡镇级国土空间规划中乡村文化场域的空间重构路径。以北京妙峰山镇为例,结合实际调查数据与社交媒体数据,从场域—资本—惯习的动态变化及三者的匹配关系出发,剖析妙峰山镇现存的社会、制度、经济与生态问题及其原因,从文化认同、文化创新、文化传承的视角提出乡村文化场域空间重构的路径,通过重构健康的制度场域和永续的社会场域、高效的经济场域、和谐的生态场域,探索新场所与乡土文化融合模式,推进乡村振兴及韧性发展。

气象灾害舆情的时空演化特征及影响因素分析

社交媒体有助于气象灾害信息传播扩散,气象灾害舆情的发展会影响公众对灾害的认知以及态度。文章以2021年“‘3·15’沙尘暴”“郑州暴雨”“台风烟花”舆情事件为研究对象,结合LDA(Latent Dirichlet Allocation,隐含狄利克雷分布)主题模型及情感词典分析探讨气象灾害舆情的时间演化特征,并基于空间自相关理论分析舆情热度的空间分布情况及其影响因素。结果表明:不同舆情阶段大众关注的主题显著不同,气象灾害舆情文本中蕴含的平均正向情绪随舆情阶段呈现递增的趋势,平均负向情绪在爆发期时达到峰值;不同地区对气象灾害关注的热度显著不同,其主要受空间距离、地区经济、网民规模等因素的影响。研究结果揭示了气象灾害舆情时空演化的普遍特征,以期为气象灾害舆情的阶段性引导及治理提供理论指导。

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。

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。

视频弹幕评论舆情分析系统设计与实现

随着用户生成内容视频网站的普及,弹幕评论成为用户表达情感性意见的重要方式。弹幕评论的即时性和互动性为网络舆情分析提供了丰富的数据资源,但其匿名性和短文本的特点给数据分析带来一定的困难。设计视频弹幕评论网络舆情分析系统,能够有效识别和过滤异常用户,并进行情感分析,最终完成对弹幕评论的主题聚类和可视化展示。系统采用前后端分离架构,前端基于Vue框架,后端使用Django框架,数据存储采用MySQL数据库。通过对Bilibili视频网站弹幕数据的实验分析,验证了系统的有效性和稳定性,促进了网络舆情分析系统的发展。

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。

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。

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。

失真信息治理情境下民众在责任归因过程中情绪的影响因素研究——以法国失真信息治理为例

[目的/意义]旨在探究失真信息治理情境下,民众在责任归因过程中影响情绪的因素及作用机理。[方法/过程]运用扎根理论分析法国失真信息治理中民众情绪的相关文献资料及数据,剖析出“情绪控制”等13个子范畴和“情绪感染”等5个主范畴,构建在责任归因过程中民众情绪的影响因素模型。[结果/结论]失真信息治理情境下,失真信息情境、情绪-价值控制、正反向评价及归因方式等因素共同对民众的情绪反应产生重要影响,并且各因素以“失真信息情境及信息素养→情绪-价值控制→情绪感染→责任归因”为逻辑主线,关系密切,可以为深入理解失真信息治理情境下民众责任归因中情绪的复杂性提供新的视角和启示。