{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/contextual-emotion-recognition-using","title":"Contextual Emotion Recognition Using Transformer-Based Models","arxiv_id":null,"date":"2023-08-02","proceeding":"INTERNATIONAL RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY 2023 8","authors":["Aayush Devgan"],"abstract":"In order to increase the precision of emotion identification in text, this research suggests a context-aware emotion recognition system employing transformer models, especially BERT. The model is able to comprehend complex emotions and context-dependent expressions since it was trained on a broad, emotion-labeled dataset. On a benchmark dataset, its efficacy is assessed compared to conventional techniques and standard transformer models. The system is proficient at gathering contextual information, and the findings demonstrate a considerable improvement in emotion recognition accuracy. This study improves textual emotion identification, opening the door to applications like chatbots that can recognize emotions and systems for tracking mental health. It also identifies potential areas for further study in developing transformer models for context-sensitive NLP applications","url_abs":"https://www.techrxiv.org/articles/preprint/Contextual_Emotion_Recognition_Using_Transformer-Based_Models/23804025","url_pdf":"https://www.techrxiv.org/articles/preprint/Contextual_Emotion_Recognition_Using_Transformer-Based_Models/23804025","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"contextual-emotion-recognition-using","repo_url":"https://github.com/aayushdevgan/contextual-emotion-recognition-using-bert","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}