Web Sentiment Analysis for Revealing Public Opinions, Trends and Making Good Financial Decisions

2013 ◽  
Author(s):  
Cristian Bissattini ◽  
Kostis Christodoulou
Keyword(s):  
Sentiment Analysis ◽  
Public Opinions ◽  
Financial Decisions

scholarly journals Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Yanni Liu ◽  
Dongsheng Liu ◽  
Yuwei Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Sentiment Analysis ◽  
Smart City ◽  
Topic Model ◽  
Rapid Development ◽  
Mobile Internet ◽  
Time Curve ◽  
Public Opinions ◽  
The Social

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.

scholarly journals Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention

2018 ◽  
Author(s):  
Ling Luo ◽  
Xiang Ao ◽  
Feiyang Pan ◽  
Jin Wang ◽  
Tong Zhao ◽  
...  
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
Neural Net ◽  
Financial Decisions ◽  
Financial Applications ◽  
Decision Making Processes ◽  
Potential Risks ◽  
Net Framework ◽  
Multiple Granularities

Sentiment analysis has played a significant role in financial applications in recent years. The informational and emotive aspects of news texts may affect the prices, volatilities, volume of trades, and even potential risks of financial subjects. Previous studies in this field mainly focused on identifying polarity~(e.g. positive or negative). However, as financial decisions broadly require justifications, only plausible polarity cannot provide enough evidence during the decision making processes of humanity. Hence an explainable solution is in urgent demand. In this paper, we present an interpretable neural net framework for financial sentiment analysis. First, we design a hierarchical model to learn the representation of a document from multiple granularities. In addition, we propose a query-driven attention mechanism to satisfy the unique characteristics of financial documents. With the domain specified questions provided by the financial analysts, we can discover different spotlights for queries from different aspects. We conduct extensive experiments on a real-world dataset. The results demonstrate that our framework can learn better representation of the document and unearth meaningful clues on replying different users? preferences. It also outperforms the state-of-the-art methods on sentiment prediction of financial documents. 

Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

2020 ◽  
pp. 939-956
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Model Fitting ◽  
Compartmental Models ◽  
Public Opinions ◽  
Estimation And Prediction

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

scholarly journals Sentiment Classification from Social Media for Stock Prediction with Data Mining

2020 ◽  
Vol 9 (5) ◽  
pp. 155-161
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Stock Price ◽  
Ensemble Methods ◽  
Economic Behavior ◽  
Sentiment Classification ◽  
Financial Decisions ◽  
Psychological Constructs ◽  
Public Sentiment ◽  
Internet Users

Social media currently plays an important role as a means of exchanging information. Through social media, information is obtained that can be used to see people's sentiments about a product or an event. Social media is a viable option to attract public sentiment through a method called sentiment analysis. The thing done is attracting sentiment from internet users through the posts made. In this way, sentiment data can be collected quickly and easily. Current economic behavior has proven that financial decisions are driven significantly by sentiment. The level of collective optimism or pessimism in society can influence investor decisions. Sentiment can also be interpreted as something that is felt by someone, both positive and negative. Sentiments and perceptions are psychological constructs and therefore difficult to measure in the analysis. This study focuses on sentiment analysis of information obtained from Twitter about stocks. For sentiment classification process ensemble methods of Naïve Bayes and SVM is used. Sentiment results are classified as positive or negative. We are expecting to see if there is connection between sentiment analysis from social media in predicting movement of IHSG stock price. As a result, we obtained strong correlation with coefficient of correlation r= 0.56609.

Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

2020 ◽  
pp. 879-896
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Model Fitting ◽  
Compartmental Models ◽  
Public Opinions ◽  
Estimation And Prediction

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

Modelling Propagation of Public Opinions on Microblogging Big Data Using Sentiment Analysis and Compartmental Models

2017 ◽  
Vol 13 (1) ◽  
pp. 11-27 ◽  
Author(s):  
Youjia Fang ◽  
Xin Chen ◽  
Zheng Song ◽  
Tianzi Wang ◽  
Yang Cao
Keyword(s):  
Social Media ◽  
Big Data ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Information Diffusion ◽  
Compartmental Model ◽  
Model Fitting ◽  
Compartmental Models ◽  
Sina Weibo ◽  
Public Opinions

Compartmental models have been used to model information diffusion on social media. However, there have been few studies on modelling positive and negative public opinions using compartmental models. This study aimed for using sentiment analysis and compartmental model to model the propagation of positive and negative opinions on microblogging big media. The authors studied the news propagation of seven popular social topics on China's Sina Weibo microblogging platform. Natural language processing and sentiment analysis were used to identify public opinions from microblogging big data. Then two existing (SIZ and SEIZ) models and a newly developed (SE2IZ) model were implemented to model the news propagation and evaluate the trends of public opinions on selected social topics. Simulation study was used to check model fitting performance. The results show that the new SE2IZ model has a better model fitting performance than existing models. This study sheds some new light on using social media for public opinion estimation and prediction.

The sentiment analysis method in finance: The psychological-financial index

2021 ◽  
Vol 27 (3) ◽  
pp. 561-584
Author(s):  
Sergei Yu. BOGATYREV
Keyword(s):  
Information Systems ◽  
Sentiment Analysis ◽  
Narrative Analysis ◽  
Measurement Techniques ◽  
Financial Analysts ◽  
Digital Economy ◽  
Opinion Poll ◽  
Financial Decisions ◽  
Measurement Framework ◽  
Financial Index

Subject. The article discusses contemporary means of measuring emotions of those who make financial decisions. Objectives. Analyzing key means of sentiment analysis in Russia and abroad, the study is to create a tool, which would be applicable to valuation and provide the unbiased information about the emotional state of those who locally make financial decisions. I also demonstrate limited capabilities of contemporary information systems in terms of emotion measurement, valuation, and present means to address the imperfection of the existing news tone measurement framework, unveil the content of new emotion measurement techniques, which would be useful to appraisers and cost analysts. Methods. The study is based on the induction and deduction for opinion poll processing, narrative analysis in data environments. I display the nexus with new technological means of modern information systems. Results. The article unveils the substance of key methods for setting the psychological-financial index, modern means of sentiment analysis in the new setting of the digital economy and Big Data. I scrutinize key constituents of the psychological-financial index and its use in the current circumstances of the post-COVID-19 economy. The article shows how psychological measurement methods can be implemented as part of the narrative analysis. Conclusions and Relevance. Financial analysts get new opportunities when using new achievements of behavioral finance and modern psychological studies. As the use of the psychological-financial index shows in analyzing market anomalies, there appear more opportunities for explaining the irrational behavior of market agents in its various segments. New standards are set as they are needed for valuation purposes, when financial analysts use them. Reporting is normalized. I provide an outlook of the analytical apparatus development and new indicators to use valuation results more efficiently. The findings hereof are applicable to the practice of contemporary appraisers, cost and fundamental analysts. It is especially important to use sentiment analysis tools in the digital economy, during the instability and crisis, change in the market paradigm, market shifts, changes in the comparability metrics, distortion of traditional financial and economic indicators, market volatility. The use of the psychological-financial index supplements and expands the scope of classical measurement tools and increase the quality of valuation.

scholarly journals Sentiment Analysis of Public Opinions on the Higher Education Expansion Policy in China

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110407
Author(s):  
Xiaoyan Yu ◽  
Shiyong Wu ◽  
Wei Chen ◽  
Mingxi Huang
Keyword(s):  
Higher Education ◽  
Sentiment Analysis ◽  
Public Opinions ◽  
Graduate Employment ◽  
Higher Education Expansion ◽  
Education Expansion ◽  
Permanent Resident ◽  
The Government ◽  
The Impact ◽  
Expansion Policy

Drawing on sentiment analysis, this study explores public opinions on the higher education expansion policy that was specifically implemented by China’s government to navigate graduate employment difficulties against the impact of COVID-19. The results indicated that the overall degree of acceptance of the expansion plan was highly positive, but some people expressed negative opinions and concerns about over-education and deferral of employment pressure. The results also suggested that the government is expected to deal with the balance between higher education expansion and graduate employment difficulties by prioritizing domestic graduate employment rather than opening up permanent resident applications for foreigners, allocating a regionally balanced expansion quota, covering social science disciplines, and creating more employment opportunities. The findings provide important suggestions for policymakers to improve policy practice and offer a referable sample for other countries in their management of graduate employment issues influenced by COVID-19.

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