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A novel deep learning model for stock market prediction using a sentiment analysis system from authoritative financial website's data.

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Title: A novel deep learning model for stock market prediction using a sentiment analysis system from authoritative financial website's data.
Authors: Chauhan, Jitendra Kumar (AUTHOR), Ahmed, Tanveer (AUTHOR), Sinha, Amit (AUTHOR)
Source: Connection Science. Dec 2025, Vol. 37 Issue 1, p1-23. 23p.
Subjects: Deep learning, Sentiment analysis, Recurrent neural networks, Computer performance, Financial databases, Stock price forecasting, Convolutional neural networks, Market sentiment
Abstract: The use of deep learning, specifically time series neural networks, in predicting stock market trends has emerged as a significant use case in financial analysis. However, the complex interrelationships and instability of the stock market have made the timely and accurate prediction of its behaviour as a confronting endeavour. To address this difficulty, in this research work a stock market index prediction model called SenT-In, which combines the with a sentiment awareness model. A sentiment awareness model using Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed to calculate the sentiment index of a large volume of news articles collected from reputable financial websites. In addition, a sentiment attention method is developed to combine stock data and news sentiment index as the input for training and predicting using the SenT-In network, which is both simple and efficient. The proposed model is evaluated in four different stock market datasets which include FSTE, SSE, Nifty 50 and S&P 500. On comparing the results with conventional deep learning algorithms such as GRU, LSTM, CNN and SVM, proposed SenT-In outperforms existing methods in accuracy with 9%, F1-Score with 7%, AUC-ROC curve with 13% and PR-AUC curve with 9% efficiency (on average). [ABSTRACT FROM AUTHOR]
Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: A novel deep learning model for stock market prediction using a sentiment analysis system from authoritative financial website's data.
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  Data: <searchLink fieldCode="AR" term="%22Chauhan%2C+Jitendra+Kumar%22">Chauhan, Jitendra Kumar</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ahmed%2C+Tanveer%22">Ahmed, Tanveer</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sinha%2C+Amit%22">Sinha, Amit</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Connection+Science%22">Connection Science</searchLink>. Dec 2025, Vol. 37 Issue 1, p1-23. 23p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sentiment+analysis%22">Sentiment analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+performance%22">Computer performance</searchLink><br /><searchLink fieldCode="DE" term="%22Financial+databases%22">Financial databases</searchLink><br /><searchLink fieldCode="DE" term="%22Stock+price+forecasting%22">Stock price forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Market+sentiment%22">Market sentiment</searchLink>
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  Label: Abstract
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  Data: The use of deep learning, specifically time series neural networks, in predicting stock market trends has emerged as a significant use case in financial analysis. However, the complex interrelationships and instability of the stock market have made the timely and accurate prediction of its behaviour as a confronting endeavour. To address this difficulty, in this research work a stock market index prediction model called SenT-In, which combines the with a sentiment awareness model. A sentiment awareness model using Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) is proposed to calculate the sentiment index of a large volume of news articles collected from reputable financial websites. In addition, a sentiment attention method is developed to combine stock data and news sentiment index as the input for training and predicting using the SenT-In network, which is both simple and efficient. The proposed model is evaluated in four different stock market datasets which include FSTE, SSE, Nifty 50 and S&P 500. On comparing the results with conventional deep learning algorithms such as GRU, LSTM, CNN and SVM, proposed SenT-In outperforms existing methods in accuracy with 9%, F1-Score with 7%, AUC-ROC curve with 13% and PR-AUC curve with 9% efficiency (on average). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Connection Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1080/09540091.2025.2455070
    Languages:
      – Code: eng
        Text: English
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        PageCount: 23
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Sentiment analysis
        Type: general
      – SubjectFull: Recurrent neural networks
        Type: general
      – SubjectFull: Computer performance
        Type: general
      – SubjectFull: Financial databases
        Type: general
      – SubjectFull: Stock price forecasting
        Type: general
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Market sentiment
        Type: general
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      – TitleFull: A novel deep learning model for stock market prediction using a sentiment analysis system from authoritative financial website's data.
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            NameFull: Chauhan, Jitendra Kumar
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            NameFull: Ahmed, Tanveer
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            – D: 01
              M: 12
              Text: Dec 2025
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              Y: 2025
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