SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 131–140 of 247 papers

TitleStatusHype
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural NetworksCode0
Term-Class-Max-Support (TCMS): A Simple Text Document Categorization Approach Using Term-Class Relevance Measure—0
Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level—0
A Novel Term_Class Relevance Measure for Text Categorization—0
Automatic Classification by Topic Domain for Meta Data Generation, Web Corpus Evaluation, and Corpus Comparison—0
Text2voronoi: An Image-driven Approach to Differential Diagnosis—0
A Personalized Markov Clustering and Deep Learning Approach for Arabic Text Categorization—0
Transductive Adaptation of Black Box Predictions—0
Analysis of opinionated text for opinion mining—0
FSMJ: Feature Selection with Maximum Jensen-Shannon Divergence for Text Categorization—0
Show:102550
← PrevPage 14 of 25Next →

No leaderboard results yet.