Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments
In recent years, the intersection between financial market behavior and social media has emerged as a sought-after source of information, meeting the requirements of investors, institutions, regulators, researchers, and policymakers. Assessing sentiment and emotions aids in evaluating public psychol...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10445412/ |
| _version_ | 1797272681439035392 |
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| author | Sarmistha Das Ujjwal Chowdhury N. S. Lijin Atulya Deep Sriparna Saha Alka Maurya |
| author_facet | Sarmistha Das Ujjwal Chowdhury N. S. Lijin Atulya Deep Sriparna Saha Alka Maurya |
| author_sort | Sarmistha Das |
| collection | DOAJ |
| description | In recent years, the intersection between financial market behavior and social media has emerged as a sought-after source of information, meeting the requirements of investors, institutions, regulators, researchers, and policymakers. Assessing sentiment and emotions aids in evaluating public psychology on particular stocks, assets, or the overall market, with shifts often aligning with market movements. Previously, machine learning, both traditional and deep learning methods, targeted discerning stock market sentiment and emotion without conducting studies to offer comprehensive explanations for these behavioral factors. In this study, we introduce a multitasking sequence-to-sequence model that integrates financial investment analysis with sentiment and emotion analysis from tweets upheld by an explanation mechanism. We also present the FinEMA dataset, featuring sentiment, emotion, and cause labels on financial stock market changes. Our study highlights how joint learning improves performance in discerning sentiment and emotion by utilizing interrelated features, enhancing task effectiveness. Our proposed model, the Emotion-Sentiment Attention Network (ESAN), achieved 89% accuracy in sentiment identification and 79% accuracy in emotion recognition, outperforming conventional machine learning methods. Furthermore, our findings indicate a positive outlook for the stock market in the latter half of 2023, which has intensified investor optimism, though some individuals still harbor uncertainties. Conclusively, our results suggest that regenerating existing computational tools can open up new research opportunities to address relevant novel tasks. The primary aim of this study is to elucidate the diverse dimensions of financial market behaviour and offer explanatory insights for the research community. The authors maintain impartiality towards specific stocks. It’s essential to note that stock market investments inherently carry market risks and potential losses. The market information within the research findings remains independent of the authors’ viewpoints. |
| first_indexed | 2024-03-07T14:32:50Z |
| format | Article |
| id | doaj.art-4c24c8d7f55e4355a76540f2d7fbed10 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T14:32:50Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4c24c8d7f55e4355a76540f2d7fbed102024-03-06T00:00:29ZengIEEEIEEE Access2169-35362024-01-0112309283094010.1109/ACCESS.2024.336903310445412Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial InvestmentsSarmistha Das0https://orcid.org/0000-0001-6608-0400Ujjwal Chowdhury1https://orcid.org/0009-0007-5655-3447N. S. Lijin2Atulya Deep3https://orcid.org/0009-0005-8780-9738Sriparna Saha4https://orcid.org/0000-0001-5494-9391Alka Maurya5CSE Department, IIT Patna, Patna, Bihta, IndiaRamakrishna Mission Vivekananda Educational and Research Institute, Belur, West Bengal, IndiaIISER Bhopal, Bhopal, Madhya Pradesh, IndiaSRM Institute of Science and Technology, Chennai, IndiaCSE Department, IIT Patna, Patna, Bihta, IndiaCrisil Pvt., Ltd., Mumbai, IndiaIn recent years, the intersection between financial market behavior and social media has emerged as a sought-after source of information, meeting the requirements of investors, institutions, regulators, researchers, and policymakers. Assessing sentiment and emotions aids in evaluating public psychology on particular stocks, assets, or the overall market, with shifts often aligning with market movements. Previously, machine learning, both traditional and deep learning methods, targeted discerning stock market sentiment and emotion without conducting studies to offer comprehensive explanations for these behavioral factors. In this study, we introduce a multitasking sequence-to-sequence model that integrates financial investment analysis with sentiment and emotion analysis from tweets upheld by an explanation mechanism. We also present the FinEMA dataset, featuring sentiment, emotion, and cause labels on financial stock market changes. Our study highlights how joint learning improves performance in discerning sentiment and emotion by utilizing interrelated features, enhancing task effectiveness. Our proposed model, the Emotion-Sentiment Attention Network (ESAN), achieved 89% accuracy in sentiment identification and 79% accuracy in emotion recognition, outperforming conventional machine learning methods. Furthermore, our findings indicate a positive outlook for the stock market in the latter half of 2023, which has intensified investor optimism, though some individuals still harbor uncertainties. Conclusively, our results suggest that regenerating existing computational tools can open up new research opportunities to address relevant novel tasks. The primary aim of this study is to elucidate the diverse dimensions of financial market behaviour and offer explanatory insights for the research community. The authors maintain impartiality towards specific stocks. It’s essential to note that stock market investments inherently carry market risks and potential losses. The market information within the research findings remains independent of the authors’ viewpoints.https://ieeexplore.ieee.org/document/10445412/Financial marketemotion and sentiment analysisexplainabilitymulti-taskingsequence-to-sequencesocial sentiment on the stock market |
| spellingShingle | Sarmistha Das Ujjwal Chowdhury N. S. Lijin Atulya Deep Sriparna Saha Alka Maurya Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments IEEE Access Financial market emotion and sentiment analysis explainability multi-tasking sequence-to-sequence social sentiment on the stock market |
| title | Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments |
| title_full | Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments |
| title_fullStr | Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments |
| title_full_unstemmed | Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments |
| title_short | Investigate How Market Behaves: Toward an Explanatory Multitasking Based Analytical Model for Financial Investments |
| title_sort | investigate how market behaves toward an explanatory multitasking based analytical model for financial investments |
| topic | Financial market emotion and sentiment analysis explainability multi-tasking sequence-to-sequence social sentiment on the stock market |
| url | https://ieeexplore.ieee.org/document/10445412/ |
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