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Sentiment Analysis On Mr

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name	Accuracy	Paper Title	Repository
VLAWE	93.3	Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation	-
RoBERTa-large 355M + Entailment as Few-shot Learner	92.5	Entailment as Few-Shot Learner	-
SGC	75.9	Simplifying Graph Convolutional Networks	-
SGCN	75.9	Simplifying Graph Convolutional Networks	-
RNN-Capsule	83.8	Sentiment Analysis by Capsules
byte mLSTM7	86.8	A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors	-
S-LSTM	76.2	Sentence-State LSTM for Text Representation	-
TM-Glove	77.51	Enhancing Interpretable Clauses Semantically using Pretrained Word Representation	-
MEAN	84.5	A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification	-
SuBiLSTM-Tied	81.6	Improved Sentence Modeling using Suffix Bidirectional LSTM	-
Millions of Emoji	-	Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm	-
AnglE-LLaMA-7B	91.09	AnglE-optimized Text Embeddings	-
SWEM-concat	78.2	Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms	-
GraphStar	76.6	Graph Star Net for Generalized Multi-Task Learning	-
Text GCN	76.74	Graph Convolutional Networks for Text Classification	-
GRU-RNN-WORD2VEC	78.26	All-but-the-Top: Simple and Effective Postprocessing for Word Representations	-
Capsule-B	82.3	Investigating Capsule Networks with Dynamic Routing for Text Classification	-
STM+TSED+PT+2L	80.09	The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning	-
USE_T+CNN	81.59	Universal Sentence Encoder	-

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Sentiment Analysis On Mr | SOTA | HyperAI