b4msa — b4msa 2.2.2 documentation
A great variety of text tasks such as topic or spam identification, user profiling, and sentiment analysis can be posed as a supervised learning problem and tackled using a text classifier. A text classifier consists of several subprocesses, some of them are general enough to be applied to any supervised learning problem, whereas others are specifically designed to tackle a particular task using complex and computational expensive processes such as lemmatization, syntactic analysis, etc. Contrary to traditi
microtc.textmodel.TextModel follows the idea of http://scikit-learn.org transformers. That is, it implements a method microtc.textmodel.TextModel.fit() that receives the training set and a method microtc.textmodel.TextModel.transform() that receives a list of texts and returns and sparse matrix that correspond to the representation of the given texts in the vector space.
@article { Tellez2018110 , title = "An automated text categorization framework based on hyperparameter optimization" , journal = "Knowledge-Based Systems" , volume = "149" , pages = "110--123" , year = "2018" , issn = "0950-7051" , doi = "10.1016/j.knosys.2018.03.003" , url = "https://www.sciencedirect.com/science/article/pii/S0950705118301217" , author = "Eric S. Tellez and Daniela Moctezuma and Sabino Miranda-Jiménez and Mario Graff" , keywords = "Text classification" , keywords = "Hyperparameter optimiza
b4msa — b4msa 2.2.2 documentation