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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 201–210 of 247 papers

TitleStatusHype
SentiHeros at SemEval-2017 Task 5: An application of Sentiment Analysis on Financial Tweets—0
SenTube: A Corpus for Sentiment Analysis on YouTube Social Media—0
Sequence-to-Set Semantic Tagging: End-to-End Multi-label Prediction using Neural Attention for Complex Query Reformulation and Automated Text Categorization—0
Sequence-to-Set Semantic Tagging for Complex Query Reformulation and Automated Text Categorization in Biomedical IR using Self-Attention—0
Sequential Learning of Convolutional Features for Effective Text Classification—0
Shortest-Path Graph Kernels for Document Similarity—0
SMM4H Shared Task 2020 - A Hybrid Pipeline for Identifying Prescription Drug Abuse from Twitter: Machine Learning, Deep Learning, and Post-Processing—0
Authorship Attribution Using Text DistortionCode0
Authorship Attribution Using the Chaos Game RepresentationCode0
Learning Graph Pooling and Hybrid Convolutional Operations for Text RepresentationsCode0
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