Comprehensive review of text-mining applications in finance

Gupta, Aaryan; Dengre, Vinya; Kheruwala, Hamza Abubakar; Shah, Manan

doi:10.1186/s40854-020-00205-1

Cited by 80 publications

(38 citation statements)

References 123 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As a structured method, teachers subjectively need to study certain learning habits and learning outcomes. Without human involvement, it is extremely difficult to test this type of learning activity using a pre-programmed smart system [ 28 , 29 ]. It should be noted that certain educational institutions are either hesitant to share their data with the blockchain network, or they find it difficult to determine which data to supply through the blockchain network.…”

Section: Challenges and Future Applicationsmentioning

confidence: 99%

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Shah¹,

Patel²,

Adesara³

et al. 2021

Vis. Comput. Ind. Biomed. Art

Self Cite

View full text Add to dashboard Cite

Although the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

show abstract

Section: Challenges and Future Applicationsmentioning

confidence: 99%

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Shah¹,

Patel²,

Adesara³

et al. 2021

Vis. Comput. Ind. Biomed. Art

Self Cite

View full text Add to dashboard Cite

show abstract

“…ANN has several beneficial capabilities and properties such as adaption capability to sight changes in adjacent environment, and nonlinear fitness to underlying physical mechanisms and to provide details about the confidence in the decision made [35]. ANN is used to process nonlinear relationships between inputs and output, which can be used in character recognition, Stock Market prediction, Self Driving cars, Image Compression, and many more [36,37].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Patel

Shah

2021

SN COMPUT. SCI.

Self Cite

View full text Add to dashboard Cite

Prediction of energy consumption and price is crucial in formatting policies related to the global energy market, demand, and supply. Data-driven analysis methods are giving rise to innovations in the world energy sector, including energy finance and economics. This paper has critically evaluated expand writings committed to Energy finance and economics applications of data-driven analysis. This paper comes up with an extensive view of state of the art in the area, which is already discussed with a different procedure. This review recognizes the applications of data-driven analysis methods in various areas such as forecasting domestic, nationwide, and transport energy consumption and price forecasting of multiple commodities, including crude oil, natural gas, and electricity. We have investigated certain research papers and given conclusion based on their researched and proposed model's prediction results and accuracies in respective areas. Our study suggests that Artificial Neural Network (ANN), Support Vector Machine (SVM), and other proposed neural network models are the most effective methods among other statistical and machine learning methods.

show abstract

“…Opinion mining can be classified based on whether a dictionary or machine learning is used to arrive at an opinion. Machine learning-based approaches require domain-specific content to work correctly, while dictionaries are costly [20]. Event analysis must be conducted by batch processing of historical events.…”

Section: Literature Surveymentioning

confidence: 99%

Building a Calendar of Events Database by Analyzing Financial Spikes

et al. 2021

View full text Add to dashboard Cite

An event is a piece of news that triggers a change in stock prices. Here, an event study is undertaken to capture the effect of abnormal returns due to an event. The event can affect the stock market in the long term or short term. Event research is relevant to both the efficient market hypothesis and behavioral finance. In this study, we collected data from websites that manage financial and economic data, performed a sentiment analysis, and correlated news article data with changes in a particular company's stock prices in the stock market. Data were collected from two well-known financial news websites. We observed a correlation between stock prices and news items. An event period of one day was considered for the study. The regression equation determined the relationship between stock returns and polarity and subjectivity. Bayesian model averaging was performed to identify the effects of polarity and subjectivity on stock returns. Time-series data were decomposed into components and detrended via regression. Prominent keywords and their polarity values for a particular day were plotted. An event enhanced some stock returns while adversely affecting other stocks. We found a variation range of -400 to 200 for different company stocks for the selected period.

show abstract

Comprehensive review of text-mining applications in finance

Cited by 80 publications

References 123 publications

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Integrating machine learning and blockchain to develop a system to veto the forgeries and provide efficient results in education sector

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Building a Calendar of Events Database by Analyzing Financial Spikes

Contact Info

Product

Resources

About