{"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/utilizing-neural-networks-and-linguistic","title":"Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences","arxiv_id":"1804.07000","date":"2018-04-19","proceeding":null,"authors":["Marcel Trotzek","Sven Koitka","Christoph M. Friedrich"],"abstract":"Depression is ranked as the largest contributor to global disability and is\nalso a major reason for suicide. Still, many individuals suffering from forms\nof depression are not treated for various reasons. Previous studies have shown\nthat depression also has an effect on language usage and that many depressed\nindividuals use social media platforms or the internet in general to get\ninformation or discuss their problems. This paper addresses the early detection\nof depression using machine learning models based on messages on a social\nplatform. In particular, a convolutional neural network based on different word\nembeddings is evaluated and compared to a classification based on user-level\nlinguistic metadata. An ensemble of both approaches is shown to achieve\nstate-of-the-art results in a current early detection task. Furthermore, the\ncurrently popular ERDE score as metric for early detection systems is examined\nin detail and its drawbacks in the context of shared tasks are illustrated. A\nslightly modified metric is proposed and compared to the original score.\nFinally, a new word embedding was trained on a large corpus of the same domain\nas the described task and is evaluated as well.","url_abs":"http://arxiv.org/abs/1804.07000v3","url_pdf":"http://arxiv.org/pdf/1804.07000v3.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":"utilizing-neural-networks-and-linguistic","repo_url":"https://github.com/gerardoasilva/NLP-DepressionDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"utilizing-neural-networks-and-linguistic","repo_url":"https://github.com/serenera/Serenera","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"word-embeddings","task_name":"Word Embeddings"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1804.07000","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}