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Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain

Sentiment analysis can be a useful tool in predicting stock market trends, as it allows us to gauge the overall sentiment towards a particular stock or company. When combined with news stored in a blockchain, sentiment analysis can provide a more accurate and trustworthy representation of the market...

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Detalles Bibliográficos
Autores principales: Sakhare, Nitin Nandkumar, Shaik, Imambi S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Nature Singapore 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257381/
http://dx.doi.org/10.1007/s41324-023-00529-x
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author Sakhare, Nitin Nandkumar
Shaik, Imambi S.
author_facet Sakhare, Nitin Nandkumar
Shaik, Imambi S.
author_sort Sakhare, Nitin Nandkumar
collection PubMed
description Sentiment analysis can be a useful tool in predicting stock market trends, as it allows us to gauge the overall sentiment towards a particular stock or company. When combined with news stored in a blockchain, sentiment analysis can provide a more accurate and trustworthy representation of the market. The aim of this paper is to study the spatial news and external events which disrupt the stock market movement as well as news analytics techniques to understand the impact of news by spatial sentiments on stock market movement. For the prediction of stock market trading decisions, a novel ensemble technique consisting of spatial federation of deep learning algorithms, machine learning algorithms and dictionary based approach is proposed to maintain privacy and geographic diversity. These learning techniques are federated into five groups and these groups are ranked as per the prediction accuracy of these models. A bit 1 or 0 is assigned for each federation thus creating a 5 bit pattern which can be used to predict stock market trading decision as strong buy, buy, hold, strong sell, sell.
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spelling pubmed-102573812023-06-12 Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain Sakhare, Nitin Nandkumar Shaik, Imambi S. Spat. Inf. Res. Article Sentiment analysis can be a useful tool in predicting stock market trends, as it allows us to gauge the overall sentiment towards a particular stock or company. When combined with news stored in a blockchain, sentiment analysis can provide a more accurate and trustworthy representation of the market. The aim of this paper is to study the spatial news and external events which disrupt the stock market movement as well as news analytics techniques to understand the impact of news by spatial sentiments on stock market movement. For the prediction of stock market trading decisions, a novel ensemble technique consisting of spatial federation of deep learning algorithms, machine learning algorithms and dictionary based approach is proposed to maintain privacy and geographic diversity. These learning techniques are federated into five groups and these groups are ranked as per the prediction accuracy of these models. A bit 1 or 0 is assigned for each federation thus creating a 5 bit pattern which can be used to predict stock market trading decision as strong buy, buy, hold, strong sell, sell. Springer Nature Singapore 2023-06-10 /pmc/articles/PMC10257381/ http://dx.doi.org/10.1007/s41324-023-00529-x Text en © The Author(s), under exclusive licence to Korea Spatial Information Society 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Sakhare, Nitin Nandkumar
Shaik, Imambi S.
Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain
title Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain
title_full Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain
title_fullStr Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain
title_full_unstemmed Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain
title_short Spatial federated learning approach for the sentiment analysis of stock news stored on blockchain
title_sort spatial federated learning approach for the sentiment analysis of stock news stored on blockchain
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10257381/
http://dx.doi.org/10.1007/s41324-023-00529-x
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