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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 101–110 of 247 papers

TitleStatusHype
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment AnalysisCode0
Feature Hashing for Language and Dialect Identification—0
Cluster Based Symbolic Representation for Skewed Text Categorization—0
Semi-supervised Text Categorization Using Recursive K-means Clustering—0
JaTeCS an open-source JAva TExt Categorization System—0
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled BagsCode0
An Automated Text Categorization Framework based on Hyperparameter OptimizationCode0
These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution—0
Discriminating between Similar Languages using Weighted Subword FeaturesCode0
Large-Scale Categorization of Japanese Product Titles Using Neural Attention Models—0
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