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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

Showing 2130 of 247 papers

TitleStatusHype
Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive StudyCode0
HFT-CNN: Learning Hierarchical Category Structure for Multi-label Short Text CategorizationCode0
A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural NetworksCode0
Inverse-Category-Frequency based supervised term weighting scheme for text categorizationCode0
Beyond original Research Articles Categorization via NLPCode0
Leap-LSTM: Enhancing Long Short-Term Memory for Text CategorizationCode0
Discriminating between Similar Languages using Weighted Subword FeaturesCode0
Multilingual Multi-class Sentiment Classification Using Convolutional Neural NetworksCode0
A Sequential Algorithm for Training Text ClassifiersCode0
Improving Pre-Trained Weights Through Meta-Heuristics Fine-TuningCode0
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