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Sentiment Classification on CR

91.4Accuracy

BYTE MLSTM

Updated 2d ago

Evaluation Results

Method	Links
BYTE MLSTM 2017.04		91.4
byte mLSTM 2018.05		90.6
SBERT 2025.12		90
CNN+MCFA 2018.06		89.4
RoBERTa (Gao et al., 2021) 2022.12		89.4
CNN+MCFA 2018.06		88.6
SCD-RoBERTa_base 2022.03		87.76
CNN+MCFA 2018.06		87.6
USE_T+CNN (w2v w.e.) 2018.03		87.45
USE T 2018.03		87.43
BERT [CLS]-embedding 2022.03		87.39
SimCSE-RoBERTa_base 2022.03		87.28
IS-BERT_base 2022.03		87.18
CNN+B1 2018.06		86.7
USE_T+DAN (w2v w.e.) 2018.03		86.66
CNN+B2 2018.06		86.6
USE_T+CNN (lrn w.e.) 2018.03		86.45
TopCNN 2018.06		86.4
AdaSent 2018.06		86.3
CNN+B2 2018.06		86.3
BiLSTM-Max (on AllNLI) 2017.05		86.3
AdaSent 2017.05		86.3
AdaSent 2015.04		86.3
ADASENT 2017.04		86.3
Avg. BERT embeddings 2022.03		86.25
CNN+B1 2018.06		86.2
CNN+B2 2018.06		86.1
USE_T+DAN (lrn w.e.) 2018.03		86.08
MC-QT 2018.05		86
CNN+B1 2018.06		85.9
SCD-BERT_base 2022.03		85.8
SimCSE-BERT_base 2022.03		85.75
CNN-Rule-q 2016.03		85.3
Capsule-B 2018.03		85.1
CNN-multichannel 2014.08		85
CNN 2018.06		85
CNN 2015.04		85
CNN 2017.04		85
CNN-Rule-p 2016.03		85
CNN-multichannel 2016.03		85
TopCNN 2018.06		84.9
TopCNN 2018.06		84.8
RoBERTa [CLS]-embedding 2022.03		84.77
CNN-static 2014.08		84.7
CNN 2018.03		84.7
CNN-non-static 2014.08		84.3
à la carte 2018.05		84.3
CNN 2018.03		84.3
CNN 2016.03		84.3
GPT-2 kNN-LM 2022.12		84.3
CNN-400 2019.05		83.82
NLM+TSED+PT+2L 2019.05		83.82
skip-thoughts 2018.05		83.8
Capsule-A 2018.03		83.8
à la carte 2018.05		83.7
SCLie 2025.12		83.6
STM+TSED+PT+2L 2019.05		83.57
LM+TSED+PT+2L 2019.05		83.54
LM+TSED+PT+2L 2019.05		83.4
NLM+TSED+PT+2L 2019.05		83.32
Tree-LSTM 2018.03		83.2
SkipThought-LN 2017.05		83.1
SKIPTHOUGHT 2017.04		83.1
NLM+TSED 2019.05		83.04
LSTM-400 2019.05		82.98
NLM+TSED 2019.05		82.96
STM+TSED 2019.05		82.96
GPT-2 + PMI 2022.12		82.8
LM+TSED 2019.05		82.76
STM+TSED+PT+2L 2019.05		82.73
CRF-PR 2014.08		82.7
CRF-PR 2016.03		82.7
BRNN 2015.04		82.6
DPCLie 2025.12		82.6
LR-LSTM 2018.03		82.5
DPCNN 2025.12		82.4
RNN 2015.04		82.3
G-Dropout 2014.08		82.1
BILSTM 2018.03		82.1
G-Dropout 2016.03		82.1
USE_D+CNN (w2v w.e.) 2018.03		82.04
CNN-LSTM 2018.05		82
USE_D+DAN (lrn w.e.) 2018.03		81.93
F-Dropout 2014.08		81.9
LM+TSED 2019.05		81.84
NBSVM 2014.08		81.8
NB-SVM 2015.04		81.8
NBSVM 2017.04		81.8
SKIPTHOUGHT 2017.04		81.8
Linear 2025.12		81.8
USE_D+DAN (w2v w.e.) 2018.03		81.71
DisC 2018.05		81.5
SCNN 2025.12		81.5
USE_D+CNN (lrn w.e.) 2018.03		81.49
Tree-CRF 2014.08		81.4
à la carte 2018.05		81.3
GrConv 2015.04		81.3
NPM 2022.12		81.2
USE D 2018.03		80.97
CNN-50 2019.05		80.89

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