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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 191–200 of 247 papers

TitleStatusHype
Reducing Over-Weighting in Supervised Term Weighting for Sentiment Analysis—0
Regularizing Text Categorization with Clusters of Words—0
RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages—0
SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization—0
Semantic Clustering and Convolutional Neural Network for Short Text Categorization—0
Semantic Similarity Computation for Abstract and Concrete Nouns Using Network-based Distributional Semantic Models—0
Semi-supervised Convolutional Neural Networks for Text Categorization via Region Embedding—0
Semi-supervised Learning with Weakly-Related Unlabeled Data : Towards Better Text Categorization—0
Semi-supervised Text Categorization Using Recursive K-means Clustering—0
SemSim: Resources for Normalized Semantic Similarity Computation Using Lexical Networks—0
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