Sentiment Analysis On Yelp Binary

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Results

Performance results of various models on this benchmark

		Paper Title
Char-level CNN	4.88	Character-level Convolutional Networks for Text Classification
SVDCNN	4.74	Squeezed Very Deep Convolutional Neural Networks for Text Classification
LEAM	4.69	Joint Embedding of Words and Labels for Text Classification
fastText, h=10, bigram	4.3	Bag of Tricks for Efficient Text Classification
SWEM-hier	4.19	Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms
SRNN	3.96	Sliced Recurrent Neural Networks
M-ACNN	3.89	Learning Context-Sensitive Convolutional Filters for Text Processing
DNC+CUW	3.60	Learning to Remember More with Less Memorization
CCCapsNet	3.52	Compositional Coding Capsule Network with K-Means Routing for Text Classification
Block-sparse LSTM	3.27	GPU Kernels for Block-Sparse Weights
CNN	2.9	Supervised and Semi-Supervised Text Categorization using LSTM for Region Embeddings
DRNN	2.73	Disconnected Recurrent Neural Networks for Text Categorization
DPCNN	2.64	Deep Pyramid Convolutional Neural Networks for Text Categorization
ULMFiT	2.16	Universal Language Model Fine-tuning for Text Classification
BERT large finetune UDA	2.05	Unsupervised Data Augmentation for Consistency Training
BERT_base+ITPT	1.92	How to Fine-Tune BERT for Text Classification?
BERT large	1.89	Unsupervised Data Augmentation for Consistency Training
LHTR	1.86	Heavy-tailed Representations, Text Polarity Classification & Data Augmentation
BERT_large+ITPT	1.81	How to Fine-Tune BERT for Text Classification?
XLNet	1.37	XLNet: Generalized Autoregressive Pretraining for Language Understanding

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Sentiment Analysis On Yelp Binary

Metrics

Results