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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 161–170 of 247 papers

TitleStatusHype
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
SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization—0
State-of-the-Art Kernels for Natural Language Processing—0
Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings—0
Supervised and Unsupervised Categorization of an Imbalanced Italian Crime News Dataset—0
Supervised learning Methods for Bangla Web Document Categorization—0
SWASH: A Naive Bayes Classifier for Tweet Sentiment Identification—0
TeamX: A Sentiment Analyzer with Enhanced Lexicon Mapping and Weighting Scheme for Unbalanced Data—0
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