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A technique to forecast Pakistan’s news using deep hybrid learning model

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Abstract

Forecasting future events is a challenging task that can have a significant impact on decision-making and policy-making. In this research, we focus on forecasting news related to Pakistan. Despite the importance of accurate predictions in this field, there currently exists no dataset for forecasting Pakistani news, specifically with regards to politics. Unlike numerical time series data, textual data includes information about the event's potential causes in addition to its impact. Better forecasts are thus anticipated as a result of this greater information. In order to address this gap, our research aims to create a first Pakistani news dataset for forecasting of Pakistan news that is mostly related to politics of Pakistan. This dataset was collected from various sources, including Pakistani news websites and social media platforms, as well as frequently asked questions about Pakistani politics. We develop a forecasting model using this dataset and evaluate the effectiveness of cutting-edge deep hybrid learning techniques incorporating neural networks, random forest, Word2vec, Natural language processing (NLP), and Naive Bayes. To the best of our understanding, no research has been done on the application of a deep hybrid learning model—a blend of deep learning and machine learning—for news forecasting. The accuracy for forecasting model is 97%. According to our findings, the model's performance is adequate when compared to that of other forecasting models. Our research not only fills the gap in the current literature but also presents a new challenge for large language models and has the potential to bring significant practical advantages in the field of forecasting. The unique contribution of this study lies in the intelligent modeling of the prediction challenge, allowing for the utilization of text rich in content for forecasting objectives.

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Authors and Affiliations

  1. University of Engineering and Technology, Lahore, 54890, Pakistan

    Rukhshanda Ihsan, Syed Khaldoon Khurshid, Muhammad Shoaib, Sadia Ali, Sana Mahnoor & Syed Muhammad Hamza

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  1. Rukhshanda Ihsan
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  2. Syed Khaldoon Khurshid
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  3. Muhammad Shoaib
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Correspondence to Rukhshanda Ihsan.

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Ihsan, R., Khurshid, S.K., Shoaib, M. et al. A technique to forecast Pakistan’s news using deep hybrid learning model. Int. j. inf. tecnol. 16, 2505–2516 (2024). https://doi.org/10.1007/s41870-024-01781-6

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