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 ...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.展开更多
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...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.展开更多
With the rapid development of social network,public opinion monitoring based on social networks is becoming more and more important.Many platforms have achieved some success in public opinion monitoring.However,these ...With the rapid development of social network,public opinion monitoring based on social networks is becoming more and more important.Many platforms have achieved some success in public opinion monitoring.However,these platforms cannot perform well in scalability,fault tolerance,and real-time performance.In this paper,we propose a novel social-network-oriented public opinion monitoring platform based on ElasticSearch(SNES).Firstly,SNES integrates the module of distributed crawler cluster,which provides real-time social media data access.Secondly,SNES integrates ElasticSearch which can store and retrieve massive unstructured data in near real time.Finally,we design subscription module based on Apache Kafka to connect the modules of the platform together in the form of message push and consumption,improving message throughput and the ability of dynamic horizontal scaling.A great number of empirical experiments prove that the platform can adapt well to the social network with highly real-time data and has good performance in public opinion monitoring.展开更多
基金funded by the Major Humanities and Social Sciences Research Projects in Zhejiang higher education institutions,grant number 2023QN082,awarded to Cheng ZhaoThe National Natural Science Foundation of China also provided funding,grant number 61902349,awarded to Cheng Zhao.
文摘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.
基金This work was supported by the Hainan Provincial Natural Science Foundation of China[2019RC041,2019RC098]National Natural Science Foundation of China[61762033]+3 种基金Hainan University Doctor Start Fund Project[kyqd1328]Hainan University Youth Fund Project[qnjj1444]Ministry of education humanities and social sciences research program fund project(19YJA710010)The Opening Project of Shanghai Trusted Industrial Control Platform(Grant No.TICPSH202003005-ZC).
文摘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.
基金This work is supported by State Grid Science and Technology Project under Grant Nos.520613180002,62061318C002the Fundamental Research Funds for the Central Universities(Grant Nos.HIT.NSRIF.201714)+4 种基金Weihai Science and Technology Development Program(2016DXGJMS15)Key Research and Development Program in Shandong Provincial(2017GGX90103)Fujian Young and Middle-aged Teacher Education Research Project,Grant No.JAT160466Jiangsu Polytechnic College of Agriculture and Forestry Key R&D Projects(2018kj11)Study and Development of Smart Agriculture Control System Based on Spark Big Data Decision(2017N0029).
文摘With the rapid development of social network,public opinion monitoring based on social networks is becoming more and more important.Many platforms have achieved some success in public opinion monitoring.However,these platforms cannot perform well in scalability,fault tolerance,and real-time performance.In this paper,we propose a novel social-network-oriented public opinion monitoring platform based on ElasticSearch(SNES).Firstly,SNES integrates the module of distributed crawler cluster,which provides real-time social media data access.Secondly,SNES integrates ElasticSearch which can store and retrieve massive unstructured data in near real time.Finally,we design subscription module based on Apache Kafka to connect the modules of the platform together in the form of message push and consumption,improving message throughput and the ability of dynamic horizontal scaling.A great number of empirical experiments prove that the platform can adapt well to the social network with highly real-time data and has good performance in public opinion monitoring.
