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Text Categorization

Text Categorization is the task of automatically assigning pre-defined categories to documents written in natural languages. Several types of Text Categorization have been studied, each of which deals with different types of documents and categories, such as topic categorization to detect discussed topics (e.g., sports, politics), spam detection, and sentiment classification to determine the sentiment typically in product or movie reviews.

Source: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

Papers

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Showing 131–140 of 247 papers

TitleStatusHype
Modeling Topic Dependencies in Hierarchical Text Categorization—0
Modeling Trolling in Social Media Conversations—0
Monitoring Energy Trends through Automatic Information Extraction—0
Muli-label Text Categorization with Hidden Components—0
Multi-Granular Text Encoding for Self-Explaining Categorization—0
Multi-label Text Categorization with Joint Learning Predictions-as-Features Method—0
Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization—0
Negative Deceptive Opinion Spam—0
N-Gramas de Caractere como T\'ecnica de Normaliza \~ao Morfol\'ogica para L\' Portuguesa: Um Estudo em Categoriza \~ao de Textos (Character N-grams as a Morphological Normalization Technique for Portuguese Language: A Study in Text Categorization)—0
Non-Standard Words as Features for Text Categorization—0
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