Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Siddique, Mohammad Mustafa; Kumar, Sandeep Title A name given to the resource BERT-Enhanced Bi-LSTM with weighted cross-entropy for multilingual sentiment classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Advances in Intelligent Informatics;Volume;11;Issue;3;pp.396-416 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.26555/ijain.v11i3.2003" target="_blank" rel="noreferrer noopener">https://doi.org/10.26555/ijain.v11i3.2003</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105015368712?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105015368712?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Siddique M.M., Christ University, Bengaluru, 560074, India; Kumar S., Christ University, Bengaluru, 560074, India Description An account of the resource With the increasing volume of multilingual user-generated content across social media platforms, effective sentiment analysis (SA) becomes crucial, especially for low-resource languages. However, traditional models relying on context-independent embeddings, such as Word2Vec, GloVe, and fastText, struggle to handle the complexity of multilingual sentiment classification. To address this, we propose an Automatic Multilingual Sentiment Detection (AMSD) framework that leverages the contextual capabilities of BERT for feature extraction and a Bidirectional Long Short-Term Memory (Bi-LSTM) network for classification. Our method, termed Elite Opposition Cross-Entropy Weighted Bi-LSTM (EOCEWBi-LSTM), integrates elite opposition-based learning to optimize hyperparameters and enhance classification accuracy. A weighted cross-entropy loss function further refines the model's sensitivity to class imbalance, thereby improving its performance. The model is trained and evaluated on the NEP_EDUSET corpus, comprising 45,434 tweets in English, Hindi, and Tamil. Experimental results demonstrate notable improvements in precision, recall, F1-score, and accuracy, highlighting the effectiveness of EOCEWBi-LSTM in multilingual sentiment analysis, especially across both high-resource and low-resource languages. The experimental results show that the proposed EOCEWBi-LSTM achieves a high F1-score ratio of 93.83% and an accuracy ratio of 93.83% compared to other existing methods. EOCEWBi-LSTM provides an effective solution for multilingual sentiment analysis, especially for languages with limited resources. 2025 The Author(s). Subject The topic of the resource BERT; Bi-LSTM; Contextual embeddings; Low-Resource languages; Multilingual sentiment analysis Publisher An entity responsible for making the resource available Universitas Ahmad Dahlan Relation A related resource ISSN: 24426571; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access; Green Open Access Format The file format, physical medium, or dimensions of the resource online