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To capture the interactions between the price and text data effectively, we create a fusion mix and utilize a hybrid information mixing module, designed using BERT and BiLSTM, to extract the multimodal interactions between the time series and semantic features. The proposed model, the hybrid information mixing module, is based on a multilayer perceptron and achieves high accuracy in predicting price fluctuations in highly volatile stock markets. Future research can extend this approach to include additional data sources and explore other deep learning techniques for better performance.<\/jats:p>","DOI":"10.3233\/jifs-231472","type":"journal-article","created":{"date-parts":[[2023,9,8]],"date-time":"2023-09-08T10:39:21Z","timestamp":1694169561000},"page":"8761-8773","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing predictive modeling for Indian banking stock trends: A fusion of BERT and attention-based BiLSTM approach"],"prefix":"10.1177","volume":"45","author":[{"given":"Arti","family":"Buche","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India"}]},{"given":"M.B.","family":"Chandak","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Shri Ramdeobaba College of Engineering and Management, Nagpur, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-231472_ref1","doi-asserted-by":"crossref","unstructured":"Varsakeli P. 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