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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

Showing 181190 of 247 papers

TitleStatusHype
Muli-label Text Categorization with Hidden Components0
Can characters reveal your native language? A language-independent approach to native language identification0
A Joint Segmentation and Classification Framework for Sentiment Analysis0
Efficient multivariate sequence classification0
A Study of Association Measures and their Combination for Arabic MWT Extraction0
Non-Standard Words as Features for Text Categorization0
Reducing Over-Weighting in Supervised Term Weighting for Sentiment Analysis0
TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data0
RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages0
Machine learning approach for text and document mining0
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