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.

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. .

The Research on Social Networks Public Opinion Propagation Influence Models and Its Controllability

Public opinion propagation control is one of the hot topics in contemporary social network research. With the rapid dissemination of information over the Internet, the traditional isolation and vaccination strategies can no longer achieve satisfactory results. A positive guidance technology for public opinion diffusion is urgently needed. First, based on the analysis of influence network controllability and public opinion diffusion, a positive guidance technology is proposed and a new model that supports external control is established. Second, in combination with the influence network, a public opinion propagation influence network model is designed and a public opinion control point selection algorithm(POCDNSA) is proposed. Finally, An experiment verified that this algorithm can lead to users receiving the correct guidance quickly and accurately, reducing the impact of false public opinion information; the effect of CELF is no better than that of the POCDNSA algorithm. The main reason is that the former is completely based on the diffusion cascade information contained in the training data, but does not consider the specific situation of the network structure and the diffusion of public opinion information in the closed set. thus, the effectiveness and feasibility of the algorithm is proven. The findings of this article therefore provide useful insights for the implementation of public opinion control.

The Transmission Mechanism and Control Strategy of the Network Public Opinion in Frontier Ethnic Regions

Social networking has become an important channel for netizens to express public Opinions and reflect social conditions and public opinions. At the same time, social networks also provide favorable conditions for the widespread dissemination of negative information. The spread of negative information will weaken the credibility of the government and affect government decisions. For this reason, the network public opinion transmission mechanism and control strategy in frontier ethnic areas are studied in depth. Through the analysis of the stage of social media public opinion dissemination, a more reasonable social media public opinion dissemination stage model was concluded, and the social media public opinion dissemination mechanism and the linkage mechanism of the dissemination mechanism were derived, which solved the problem of how social media sentiment was disseminated. According to the analysis of the stage characteristics of social media public opinion dissemination , the six stages of public opinion transmission were divided into different levels , and corresponding strategies were proposed for different levels of the dissemination stage.

A Model for Cross-Domain Opinion Target Extraction in Sentiment Analysis

Opinion target extraction is one of the core tasks in sentiment analysison text data. In recent years, dependency parser–based approaches have beencommonly studied for opinion target extraction. However, dependency parsersare limited by language and grammatical constraints. Therefore, in this work, asequential pattern-based rule mining model, which does not have such constraints,is proposed for cross-domain opinion target extraction from product reviews inunknown domains. Thus, knowing the domain of reviews while extracting opinion targets becomes no longer a requirement. The proposed model also revealsthe difference between the concepts of opinion target and aspect, which are commonly confused in the literature. The model consists of two stages. In the firststage, the aspects of reviews are extracted from the target domain using the rulesautomatically generated from source domains. The aspects are also transferredfrom the source domains to a target domain. Moreover, aspect pruning is appliedto further improve the performance of aspect extraction. In the second stage, theopinion target is extracted among the aspects extracted at the former stage usingthe rules automatically generated for opinion target extraction. The proposedmodel was evaluated on several benchmark datasets in different domains andcompared against the literature. The experimental results revealed that the opiniontargets of the reviews in unknown domains can be extracted with higher accuracythan those of the previous works.

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining(OM)concepts become familiar due to advances in networking technologies and social media.Recently,massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult.Since OM find useful in business sectors to improve the quality of the product as well as services,machine learning(ML)and deep learning(DL)models can be considered into account.Besides,the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process.Therefore,in this paper,a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory(AFSO-BLSTM)model has been developed for OM process.The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data.In addition,the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process.Besides,BLSTM model is employed for the effectual detection and classification of opinions.Finally,the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model,shows the novelty of the work.A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.

Sentiment Analysis Based on Performance of Linear Support Vector Machine and Multinomial Naïve Bayes Using Movie Reviews with Baseline Techniques

Movies are the better source of entertainment.Every year,a great percentage of movies are released.People comment on movies in the form of reviews after watching them.Since it is difficult to read all of the reviews for a movie,summarizing all of the reviews will help make this decision without wasting time in reading all of the reviews.Opinion mining also known as sentiment analysis is the process of extracting subjective information from textual data.Opinion mining involves identifying and extracting the opinions of individuals,which can be positive,neutral,or negative.The task of opinion mining also called sentiment analysis is performed to understand people’s emotions and attitudes in movie reviews.Movie reviews are an important source of opinion data because they provide insight into the general public’s opinions about a particular movie.The summary of all reviews can give a general idea about the movie.This study compares baseline techniques,Logistic Regression,Random Forest Classifier,Decision Tree,K-Nearest Neighbor,Gradient Boosting Classifier,and Passive Aggressive Classifier with Linear Support Vector Machines and Multinomial Naïve Bayes on the IMDB Dataset of 50K reviews and Sentiment Polarity Dataset Version 2.0.Before applying these classifiers,in pre-processing both datasets are cleaned,duplicate data is dropped and chat words are treated for better results.On the IMDB Dataset of 50K reviews,Linear Support Vector Machines achieve the highest accuracy of 89.48%,and after hyperparameter tuning,the Passive Aggressive Classifier achieves the highest accuracy of 90.27%,while Multinomial Nave Bayes achieves the highest accuracy of 70.69%and 71.04%after hyperparameter tuning on the Sentiment Polarity Dataset Version 2.0.This study highlights the importance of sentiment analysis as a tool for understanding the emotions and attitudes in movie reviews and predicts the performance of a movie based on the average sentiment of all the reviews.

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。

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。

考虑用户能动性和流动性的舆情传播模型

针对现有信息传播模型忽略了用户主观能动性和社交网络动态性的问题,提出异构网络中考虑用户能动性和流动性的SCBRD(Susceptible-Commented-Believed-Recovered-Defensed)舆情传播模型。首先,利用下一代矩阵方法计算基本再生数,并运用Lyapunov稳定性定理和庞特里亚金原理分析了系统的动力学和最优控制问题。然后,基于BA(Barabási-Albert)无标度网络进行仿真分析以确定影响舆情传播的显著因素,结果表明,用户的好奇心理、转发行为和进入率在信息扩散中起着主导作用,并且系统存在最优控制解。最后,依据实际数据验证模型的合理性。与SCIR(Susceptible-inCubation-Infective-Refractory)模型相比,SCBRD模型的拟合优度提高了27.40%,预测的均方根误差(RMSE)减小了39.02%。因此,该模型能够适应信息传播复杂的变化形势,为官方的舆情监管提供较好的指导。

^(*)ST中利内部控制否定意见案例分析

近年来,被出具否定意见内部控制审计报告的上市公司数量日益增长,公司如何更好地运用内部控制审计报告来推动自身内部控制质量的提升成为一个重要议题。本文以^(*)ST中利(股票代码002309)为研究对象,主要针对其2022年被出具内部控制审计否定意见后所披露出的缺陷和整改情况,从内部控制五要素方面分析^(*)ST中利内部控制缺陷的产生原因,并提出上市公司应优化组织结构,完善关联方资金占用管理制度;重视事前防控,加强关联方交易风险评估;落实授权审批,保证控制活动有效执行;完善沟通渠道,提高公司信息披露质量;重视内审人员,强化内部监督等建议。

多重约束下民警公务用枪的行动逻辑

公务用枪中存在的问题反映出公安机关在社会治理中的困境。实证研究发现,民警公务用枪的心理与行为受到多重条件的约束。文化与舆论是最外围的软性约束,影响着公务用枪的正义感。法律法规是较外围的刚性约束,决定着公务用枪的合法感。组织氛围是较内围的显性约束,决定着公务用枪的支持感。素质与训练则是最内围的硬性约束,决定着公务用枪的效能感。四个维度的用枪心理影响着民警在警务实战中的行为倾向和行为表现。涉枪事件的应对和涉枪民警的处理,又将反作用于涉枪民警及其他民警的心理与行为。研究结果为民警不愿拿枪、不敢用枪、不会用枪等问题找到一种阐释框架。

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。

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。

基于主动安全的重大事故网络舆情智能建模与仿真

针对重大安全事故发生时,肆意传播且真伪难辨的信息易引起社会负面情绪,给应急处置及救援工作带来不便的问题,基于主动安全理念,结合情感分析模型、主题计算模型和易感人群-潜伏人群-感染人群-离去人群(SEIR)模型,开展重大安全事故网络舆情仿真及控制策略研究。运用卷积深度置信网络(CDBN)、时域卷积网络(TCN)、条件随机场(CRF)组成的CDBN-TCN-CRF情感分析模型及T分布瓦瑟斯坦自编码器(TWAE)主题计算模型,识别情感极性及主题类别,跟踪网络舆情情感倾向及民众关注热点;运用SEIR模型来预测网络舆情走势,并研究网络舆情的传播过程和影响因素。结果表明:CDBN-TCN-CRF情感分析模型、TWAE主题计算模型及SEIR模型结合使用,可以更好地展现其对网络舆情深度分析与趋势预测的能力。

内地青年网络社群涉台舆情的主题分布与情感特征——基于LDA模型和文本情感计算的大数据分析

综合使用LDA模型、文本情感计算等大数据方法与传统数据处理方法,分析内地主流青年网络社群中的舆情文本后发现,当前青年网络社群中的涉台议题表达可归入“政治”“文娱”“对抗”“生活”“交流”五大主题。在内容特征方面,“政治”和“交流”主题较易获得观看者的支持;“文娱”议题呈现“粉丝向”特征;“对抗”议题较易获得短期关注与转发;“生活”议题较易引发广泛讨论。在情感特征方面,涉台讨论呈现出“爱憎分明”的特点,最常被表达的情感是“赞扬”和“贬责”,但在积极向度的表达中,约有20%—25%属于揶揄和讽刺性用法。在互动形式方面,负面、强烈的情感表达较易引发关注和讨论。另外,用户性别与涉台讨论的情感向度显著相关。未来有必要进一步加强对青年网络社群的涉台舆情追踪,重视舆情引导,并强化青年网络社群中涉台交流的机制建设和积极情感表达。

基于情感-主题协同演化模型的突发信息安全事件网络舆情分析

[目的/意义]针对突发信息安全事件,探索舆情生命周期中各阶段公众的情感倾向与关注的热点主题,快速挖掘网络舆情演化特征与发展趋势,有助于政府、企业和相关部门对舆情的监测与处理。[方法/过程]以滴滴事件为例,首先搜集事件相关微博评论文本,对舆情演化周期进行阶段划分,使用基于改进TF-IDF方法和LDA模型对各阶段进行主题挖掘,并构建融入领域情感词与表情符号的情感词典对各阶段下不同主题进行情感分析,得到舆情周期内主题与情感特征的协同演化趋势。[结果/结论]所提研究方法得到的舆情演化趋势能够有效反映突发事件各阶段的主题情感特征,有助于引导管控网络舆情,为舆情治理措施的制定提供科学依据。

基于智能体异质影响的观点一致性控制研究

针对传统观点模型中个体影响能力相同的简单设定,本文基于社会网络结构的拓扑属性,将节点度相关的时变指数权重引入连续观点模型,提出异质权重观点演化动力学模型,研究多智能体系统中的个体异质性对观点演化行为的影响,并从网络拓扑结构角度对观点一致性进行分析和控制.仿真结果表明与传统模型相比,网络个体的异质性相互作用极大地改变了观点演化状态,促进了整个系统的观点一致性达成.随着权重指数增大,个体的异质性影响能力增加,使网络连通性增强.网络中节点度较大的个体的主导作用促使观点能够更快地收敛达成一致,从而提高了系统收敛效率.与固定权重观点模型相比,权重随观点的动态演化使系统能够在相对有限的交互邻域内更快地收敛.

融合BERT多层次特征的短视频网络舆情情感分析研究

自媒体时代与网络社交软件的广泛普及,导致短视频平台极易成为舆情事件起源和发酵的“孵化器”。分析短视频平台中的舆情评论信息,对于舆情事件的预警、处置和引导具有重要意义。鉴于此,结合BERT与TextCNN模型,提出一种融合BERT多层次特征的文本分类模型(BERT-MLFF-TextCNN),并对抖音短视频平台中的相关评论文本数据进行情感分析。首先,利用BERT预训练模型对输入文本进行编码。其次,提取各编码层中的语义特征向量进行融合。然后,融入自注意力机制突出其关键特征,从而实现特征的有效利用。最后,将所得特征序列输入TextCNN模型中进行分类。实验结果表明,与BERT-TextCNN、GloVe-TextCNN和Word2vec-TextCNN模型相比,BERT-MLFF-TextCNN模型表现更优,F1值达到了0.977。通过该模型能够有效识别短视频平台舆情的情感倾向,在此基础上利用TextRank算法进行主题挖掘,实现舆情评论情感极性的主题词可视化,为相关部门的舆情管控工作提供决策参考。