{"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/attention-based-lstm-for-psychological-stress","title":"Attention-Based LSTM for Psychological Stress Detection from Spoken Language Using Distant Supervision","arxiv_id":"1805.12307","date":"2018-05-31","proceeding":null,"authors":["Genta Indra Winata","Onno Pepijn Kampman","Pascale Fung"],"abstract":"We propose a Long Short-Term Memory (LSTM) with attention mechanism to\nclassify psychological stress from self-conducted interview transcriptions. We\napply distant supervision by automatically labeling tweets based on their\nhashtag content, which complements and expands the size of our corpus. This\nadditional data is used to initialize the model parameters, and which it is\nfine-tuned using the interview data. This improves the model's robustness,\nespecially by expanding the vocabulary size. The bidirectional LSTM model with\nattention is found to be the best model in terms of accuracy (74.1%) and\nf-score (74.3%). Furthermore, we show that distant supervision fine-tuning\nenhances the model's performance by 1.6% accuracy and 2.1% f-score. The\nattention mechanism helps the model to select informative words.","url_abs":"http://arxiv.org/abs/1805.12307v1","url_pdf":"http://arxiv.org/pdf/1805.12307v1.pdf","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":"attention-based-lstm-for-psychological-stress","repo_url":"https://github.com/brianferrell787/Visualizing-attention-layer-in-text-classification-deep-learning-algorithim","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"attention-based-lstm-for-psychological-stress","repo_url":"https://github.com/brianferrell787/Visualizing-attention-layer-in-text-classification-deep-learning-algorithm","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"attention-based-lstm-for-psychological-stress","repo_url":"https://github.com/gentaiscool/lstm-attention","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"attention-based-lstm-for-psychological-stress","repo_url":"https://github.com/kihongmin/NLP_Classifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1805.12307","atlas_url":"https://app.syntology.ai/?focus=1805.12307","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}