{"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/an-automated-text-categorization-framework","title":"An Automated Text Categorization Framework based on Hyperparameter Optimization","arxiv_id":"1704.01975","date":"2017-04-06","proceeding":null,"authors":["Eric S. Tellez","Daniela Moctezuma","Sabino Miranda-Jímenez","Mario Graff"],"abstract":"A great variety of text tasks such as topic or spam identification, user\nprofiling, and sentiment analysis can be posed as a supervised learning problem\nand tackle using a text classifier. A text classifier consists of several\nsubprocesses, some of them are general enough to be applied to any supervised\nlearning problem, whereas others are specifically designed to tackle a\nparticular task, using complex and computational expensive processes such as\nlemmatization, syntactic analysis, etc. Contrary to traditional approaches, we\npropose a minimalistic and wide system able to tackle text classification tasks\nindependent of domain and language, namely microTC. It is composed by some easy\nto implement text transformations, text representations, and a supervised\nlearning algorithm. These pieces produce a competitive classifier even in the\ndomain of informally written text. We provide a detailed description of microTC\nalong with an extensive experimental comparison with relevant state-of-the-art\nmethods. mircoTC was compared on 30 different datasets. Regarding accuracy,\nmicroTC obtained the best performance in 20 datasets while achieves competitive\nresults in the remaining 10. The compared datasets include several problems\nlike topic and polarity classification, spam detection, user profiling and\nauthorship attribution. Furthermore, it is important to state that our approach\nallows the usage of the technology even without knowledge of machine learning\nand natural language processing.","url_abs":"http://arxiv.org/abs/1704.01975v2","url_pdf":"http://arxiv.org/pdf/1704.01975v2.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":"an-automated-text-categorization-framework","repo_url":"https://github.com/INGEOTEC/microTC","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"authorship-attribution","task_name":"Authorship Attribution"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"hyperparameter-optimization","task_name":"Hyperparameter Optimization"},{"task_slug":"lemmatization","task_name":"Lemmatization"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"spam-detection","task_name":"Spam detection"},{"task_slug":"text-categorization","task_name":"Text Categorization"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}