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 Ara, Affreen; Rekha, V. Title A name given to the resource Enhancing Music Emotion Recognition with LSTM: Evaluating Various Embedding Techniques 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 SSRG International Journal of Electronics and Communication Engineering;Volume;12;Issue;6;pp.293-303 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/23488549/IJECE-V12I6P123" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/23488549/IJECE-V12I6P123</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105009850939?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105009850939?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 Ara A., Department of Computer Science and Engineering, Christ University, Karnataka, India; Rekha V., Department of Computer Science and Engineering, Christ University, Karnataka, India Description An account of the resource The study investigates the application of Long Short-Term Memory (LSTM) networks for emotion classification in music lyrics. It focuses on the comparative effectiveness of various word embedding techniques. It evaluates the performance of static embeddings (GloVe, Word2Vec, FastText) versus contextual embeddings (BERT, Distil BERT) across three datasets: MER Lyrics, Mood Lyrics, and Combined Lyrics. Additionally, the study examines the role of stylistic and content-based features in enhancing classification accuracy. The results demonstrate that contextual embeddings considerably outperform static embeddings, achieving accuracy rates of up to 98% compared to 60% for static approaches. Moreover, combining multiple lyric datasets leads to improved model generalization. The findings show the potential of transformer-based models for advancing music emotion recognition. Future research will focus on optimizing large embedding models using techniques such as pruning, quantization, and distillation to enhance computational efficiency. 2025 Seventh Sense Research Group. Subject The topic of the resource BERT; Embedding; Emotion classification and Emotion; LSTM Publisher An entity responsible for making the resource available Seventh Sense Research Group Relation A related resource ISSN: 23488549; 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 Format The file format, physical medium, or dimensions of the resource online