{"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/sentiment-analysis-of-code-mixed-languages","title":"Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich Languages","arxiv_id":"1804.00806","date":"2018-04-03","proceeding":null,"authors":["Nurendra Choudhary","Rajat Singh","Ishita Bindlish","Manish Shrivastava"],"abstract":"Code-mixed data is an important challenge of natural language processing\nbecause its characteristics completely vary from the traditional structures of\nstandard languages.\n  In this paper, we propose a novel approach called Sentiment Analysis of\nCode-Mixed Text (SACMT) to classify sentences into their corresponding\nsentiment - positive, negative or neutral, using contrastive learning. We\nutilize the shared parameters of siamese networks to map the sentences of\ncode-mixed and standard languages to a common sentiment space. Also, we\nintroduce a basic clustering based preprocessing method to capture variations\nof code-mixed transliterated words. Our experiments reveal that SACMT\noutperforms the state-of-the-art approaches in sentiment analysis for\ncode-mixed text by 7.6% in accuracy and 10.1% in F-score.","url_abs":"http://arxiv.org/abs/1804.00806v1","url_pdf":"http://arxiv.org/pdf/1804.00806v1.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":"sentiment-analysis-of-code-mixed-languages","repo_url":"https://github.com/mankadronit/60DaysofUdacity-Challenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"contrastive-learning","task_name":"Contrastive Learning"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}