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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 11–20 of 247 papers

TitleStatusHype
A Machine Learning Framework for Authorship Identification From Texts—0
A Machine Learning Method to Distinguish Machine Translation from Human Translation—0
A Method of Accounting Bigrams in Topic Models—0
A Deep Learning Model with Hierarchical LSTMs and Supervised Attention for Anti-Phishing—0
A Hidden Variables Approach to Multilabel Logistic Regression—0
A Composite Kernel Approach for Dialog Topic Tracking with Structured Domain Knowledge from Wikipedia—0
An approach based on Combination of Features for automatic news retrieval—0
A New Feature Selection Technique Combined with ELM Feature Space for Text Classification—0
An Examination of Regret in Bullying Tweets—0
Analysis of opinionated text for opinion mining—0
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