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Sentiment Classification

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Showing 551–600 of 949 papers

TitleStatusHype
Simultaneous Identification of Tweet Purpose and Position—0
Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds—0
SemEval-2016 Task 4: Sentiment Analysis in TwitterCode0
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text—0
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU—0
Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus—0
Contextual Recurrent Units for Cloze-style Reading Comprehension—0
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting SolutionCode0
Multi-Task Stance Detection with Sentiment and Stance Lexicons—0
A Comparative Analysis of Unsupervised Language Adaptation Methods—0
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification—0
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews—0
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification—0
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks—0
To Annotate or Not? Predicting Performance Drop under Domain Shift—0
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree—0
A Robust Self-Learning Framework for Cross-Lingual Text Classification—0
Natural Language Generation for Effective Knowledge DistillationCode0
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis—0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
Predicting Discourse Structure using Distant Supervision from Sentiment—0
Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification—0
A Game Theoretic Approach to Class-wise Selective RationalizationCode0
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer EncodersCode0
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification—0
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning—0
Fine-grained Sentiment Classification using BERTCode0
The merits of Universal Language Model Fine-tuning for Small Datasets -- a case with Dutch book reviewsCode0
Machine Translation for Machines: the Sentiment Classification Use Case—0
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health—0
Amharic Negation Handling—0
Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution NetworkCode0
Text Length Adaptation in Sentiment ClassificationCode0
Dual Adversarial Co-Learning for Multi-Domain Text Classification—0
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model—0
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification—0
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation—0
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional NetworksCode0
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks—0
Transfer Learning Between Related Tasks Using Expected Label ProportionsCode0
Cross-Domain Sentiment Classification using Vector Embedded Domain Representations—0
Learning with Noisy Labels for Sentence-level Sentiment Classification—0
NEZHA: Neural Contextualized Representation for Chinese Language UnderstandingCode0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
Rethinking Attribute Representation and Injection for Sentiment ClassificationCode0
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification—0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Fine-grained Sentiment Analysis with Faithful Attention—0
TDAM: a Topic-Dependent Attention Model for Sentiment Analysis—0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding—0
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