Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT
In an era inundated with vast amounts of information, the imperative for efficient news classification is paramount. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with Generative Pre-trained Transformers (GPT) to enhance the precision...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-11-01
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| Series: | Journal of Economy and Technology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949948824000404 |
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| author | Fahim Sufi |
| author_facet | Fahim Sufi |
| author_sort | Fahim Sufi |
| collection | DOAJ |
| description | In an era inundated with vast amounts of information, the imperative for efficient news classification is paramount. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with Generative Pre-trained Transformers (GPT) to enhance the precision and efficacy of news categorization. The rapid digital dissemination of news necessitates advanced computational methodologies capable of accurate classification and event prediction that include finance and economic events. Leveraging recent advancements in machine learning and natural language processing (NLP), this study utilizes large language models (LLMs) such as GPT and BERT, known for their exceptional comprehension and generation of human-like text. Over 232 days, our methodology classified 33,979 news articles into Education & Learning, Health & Medicine, and Science & Technology, with further subcategorization into 32 distinct subcategories. For evaluation, a sample of 5000 articles was assessed using metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Precision, Recall, and F1-Score. In comparison with the existing studies, the proposed method achieving significantly higher with average scores of 0.986 (Precision), 0.987 (Recall), and 0.987 (F1-Score). This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. The theoretical contributions are profound, demonstrating the mathematical integration of GPT with CNNs and recurrent neural networks. This integration advances computational news classification and exemplifies how sophisticated mathematical frameworks enhance large-scale text data analysis, marking a pivotal advancement in applying advanced computational methods in real-world scenarios. |
| format | Article |
| id | doaj-art-b017c3aa56164fcf9b3e5e862370cd6a |
| institution | DOAJ |
| issn | 2949-9488 |
| language | English |
| publishDate | 2025-11-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Economy and Technology |
| spelling | doaj-art-b017c3aa56164fcf9b3e5e862370cd6a2025-08-20T02:39:31ZengKeAi Communications Co., Ltd.Journal of Economy and Technology2949-94882025-11-01326428110.1016/j.ject.2024.09.001Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPTFahim Sufi0Corresponding author.; Monash University, Melbourne, VIC 3004, AustraliaIn an era inundated with vast amounts of information, the imperative for efficient news classification is paramount. This research explores the sophisticated integration of neural networks and convolutional neural networks (CNN) with Generative Pre-trained Transformers (GPT) to enhance the precision and efficacy of news categorization. The rapid digital dissemination of news necessitates advanced computational methodologies capable of accurate classification and event prediction that include finance and economic events. Leveraging recent advancements in machine learning and natural language processing (NLP), this study utilizes large language models (LLMs) such as GPT and BERT, known for their exceptional comprehension and generation of human-like text. Over 232 days, our methodology classified 33,979 news articles into Education & Learning, Health & Medicine, and Science & Technology, with further subcategorization into 32 distinct subcategories. For evaluation, a sample of 5000 articles was assessed using metrics such as True Positive (TP), True Negative (TN), False Positive (FP), False Negative (FN), Precision, Recall, and F1-Score. In comparison with the existing studies, the proposed method achieving significantly higher with average scores of 0.986 (Precision), 0.987 (Recall), and 0.987 (F1-Score). This research offers substantial practical contributions, providing detailed insights into news source contributions, effective anomaly detection, and predictive trend analysis using neural networks. The theoretical contributions are profound, demonstrating the mathematical integration of GPT with CNNs and recurrent neural networks. This integration advances computational news classification and exemplifies how sophisticated mathematical frameworks enhance large-scale text data analysis, marking a pivotal advancement in applying advanced computational methods in real-world scenarios.http://www.sciencedirect.com/science/article/pii/S2949948824000404Computational methodsNews report intelligenceGPTEvent predictionAnomaly detectionCNN |
| spellingShingle | Fahim Sufi Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT Journal of Economy and Technology Computational methods News report intelligence GPT Event prediction Anomaly detection CNN |
| title | Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT |
| title_full | Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT |
| title_fullStr | Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT |
| title_full_unstemmed | Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT |
| title_short | Advanced computational methods for news classification: A study in neural networks and CNN integrated with GPT |
| title_sort | advanced computational methods for news classification a study in neural networks and cnn integrated with gpt |
| topic | Computational methods News report intelligence GPT Event prediction Anomaly detection CNN |
| url | http://www.sciencedirect.com/science/article/pii/S2949948824000404 |
| work_keys_str_mv | AT fahimsufi advancedcomputationalmethodsfornewsclassificationastudyinneuralnetworksandcnnintegratedwithgpt |