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Sentiment Classification

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TitleStatusHype
An Introductory Survey on Attention Mechanisms in NLP Problems—0
Stronger Data Poisoning Attacks Break Data Sanitization DefensesCode1
Recurrent Attention Unit—0
On Zero-shot Cross-lingual Transfer of Multilingual Neural Machine Translation—0
Super Characters: A Conversion from Sentiment Classification to Image Classification—0
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings—0
Learning representations for sentiment classification using Multi-task framework—0
Ternary Twitter Sentiment Classification with Distant Supervision and Sentiment-Specific Word Embeddings—0
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings—0
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification—0
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning—0
Multi-grained Attention Network for Aspect-Level Sentiment Classification—0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations—0
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network—0
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification—0
Siamese Network-Based Supervised Topic Modeling—0
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment AnalysisCode0
Sentiment Classification towards Question-Answering with Hierarchical Matching Network—0
Memory, Show the Way: Memory Based Few Shot Word Representation Learning—0
Cell-aware Stacked LSTMs for Modeling Sentences—0
Adaptive Semi-supervised Learning for Cross-domain Sentiment ClassificationCode0
Iterative Recursive Attention Model for Interpretable Sequence Classification—0
Distance Based Source Domain Selection for Sentiment Classification—0
Revisiting the Importance of Encoding Logic Rules in Sentiment ClassificationCode0
Using J-K-fold Cross Validation To Reduce Variance When Tuning NLP ModelsCode0
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