Sentiment Analysis Edit Task

Natural Language Processing

627

papers with code

benchmarks

datasets

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Sentiment analysis is the task of classifying the polarity of a given text.

Benchmarks

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DATASET	BEST METHOD	PAPER TITLE	COMPARE
SST-2 Binary classification	SMART-RoBERTa Large	SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization	See all
IMDb	NB-weighted-BON + dv-cosine	Sentiment Classification Using Document Embeddings Trained with Cosine Similarity	See all
SST-5 Fine-grained classification	RoBERTa-large+Self-Explaining	Self-Explaining Structures Improve NLP Models	See all
Yelp Binary classification	XLNet	XLNet: Generalized Autoregressive Pretraining for Language Understanding	See all
Yelp Fine-grained classification	XLNet	XLNet: Generalized Autoregressive Pretraining for Language Understanding	See all
MR	byte mLSTM7	A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors	See all
Amazon Review Polarity	BERT large	Unsupervised Data Augmentation for Consistency Training	See all
Amazon Review Full	BERT large	Unsupervised Data Augmentation for Consistency Training	See all
User and product information	BiLSTM+CHIM	Rethinking Attribute Representation and Injection for Sentiment Classification	See all
SemEval 2014 Task 4 Subtask 1+2	GRACE	GRACE: Gradient Harmonized and Cascaded Labeling for Aspect-based Sentiment Analysis	See all
CR	Block-sparse LSTM	GPU Kernels for Block-Sparse Weights	See all
Multi-Domain Sentiment Dataset	Distributional Correspondence Indexing	Revisiting Distributional Correspondence Indexing: A Python Reimplementation and New Experiments	See all
MPQA	STM+TSED+PT+2L	The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning	See all
SemEval 2017 Task 4-A	LSTMs+CNNs ensemble with multiple conv. ops	BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs	See all
DBRD	RobBERT v2	RobBERT: a Dutch RoBERTa-based Language Model	See all
Twitter	AEN-BERT	Attentional Encoder Network for Targeted Sentiment Classification	See all
FiQA	FinBERT	FinBERT: Financial Sentiment Analysis with Pre-trained Language Models	See all
RuSentiment	RuBERT-RuSentiment	Deep Transfer Learning Baselines for Sentiment Analysis in Russian	See all
SemEval	LSTMs+CNNs ensemble with multiple conv. ops	BB_twtr at SemEval-2017 Task 4: Twitter Sentiment Analysis with CNNs and LSTMs	See all
Sogou News	fastText, h=10, bigram	Bag of Tricks for Efficient Text Classification	See all
ASTD	CNN-LSTM	Mazajak: An Online Arabic Sentiment Analyser	See all
ArSAS	CNN-LSTM	Mazajak: An Online Arabic Sentiment Analyser	See all
ChnSentiCorp Dev	RoBERTa-wwm-ext-large	Pre-Training with Whole Word Masking for Chinese BERT	See all
ChnSentiCorp	RoBERTa-wwm-ext-large	Pre-Training with Whole Word Masking for Chinese BERT	See all
Financial PhraseBank	FinBERT	FinBERT: Financial Sentiment Analysis with Pre-trained Language Models	See all
HARD	AraBERTv1	AraBERT: Transformer-based Model for Arabic Language Understanding	See all
AJGT	AraBERTv1	AraBERT: Transformer-based Model for Arabic Language Understanding	See all
LABR (2-class, unbalanced)	AraBERTv1	AraBERT: Transformer-based Model for Arabic Language Understanding	See all
20 Newsgroups	Emotive Lexicon	ASTD: Arabic Sentiment Tweets Dataset	See all
Latvian Twitter Eater Sentiment Dataset	Naive Bayes	What Can We Learn From Almost a Decade of Food Tweets	See all

Libraries

AllenNLP

9,928 stars · 24 models

Subtasks

Aspect-Based Sentiment Analysis

19 benchmarks

74 papers with code

Multimodal Sentiment Analysis

3 benchmarks

27 papers with code

Twitter Sentiment Analysis

1 benchmark

7 papers with code

Aspect Sentiment Triplet Extraction

1 benchmark

4 papers with code

Arabic Sentiment Analysis

3 papers with code

See all 8 subtasks

Datasets

MR

GLUE

SST

IMDb Movie Reviews

See all 53 datasets

Greatest papers with code

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

NAACL 2019 • tensorflow/models •

We introduce a new language representation model called BERT, which stands for Bidirectional Encoder Representations from Transformers.

Ranked #1 on Question Answering on CoQA

COMMON SENSE REASONING CONVERSATIONAL RESPONSE SELECTION CROSS-LINGUAL NATURAL LANGUAGE INFERENCE NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING SENTENCE CLASSIFICATION SENTIMENT ANALYSIS

69,521

Paper
Code

Adversarial Training Methods for Semi-Supervised Text Classification

25 May 2016 • tensorflow/models •

Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting.

Ranked #12 on Sentiment Analysis on IMDb

SEMI SUPERVISED TEXT CLASSIFICATION SEMI-SUPERVISED TEXT CLASSIFICATION SENTIMENT ANALYSIS WORD EMBEDDINGS

69,521

Paper
Code

Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

HLT 2015 • tensorflow/models •

Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data.

Ranked #15 on Sentiment Analysis on IMDb

SENTIMENT ANALYSIS

69,521

Paper
Code

DeBERTa: Decoding-enhanced BERT with Disentangled Attention

ICLR 2021 • huggingface/transformers •

Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks.

Ranked #1 on Question Answering on SQuAD1.1 dev

COMMON SENSE REASONING COREFERENCE RESOLUTION LINGUISTIC ACCEPTABILITY NAMED ENTITY RECOGNITION NATURAL LANGUAGE INFERENCE NATURAL LANGUAGE UNDERSTANDING QUESTION ANSWERING READING COMPREHENSION SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS WORD SENSE DISAMBIGUATION

44,554

Paper
Code

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

NeurIPS 2019 • huggingface/transformers •

As Transfer Learning from large-scale pre-trained models becomes more prevalent in Natural Language Processing (NLP), operating these large models in on-the-edge and/or under constrained computational training or inference budgets remains challenging.

Ranked #8 on Semantic Textual Similarity on MRPC

KNOWLEDGE DISTILLATION LANGUAGE MODELLING LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING SEMANTIC TEXTUAL SIMILARITY SENTIMENT ANALYSIS TRANSFER LEARNING

44,554

Paper
Code

RoBERTa: A Robustly Optimized BERT Pretraining Approach

26 Jul 2019 • huggingface/transformers •

Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging.

Ranked #2 on Common Sense Reasoning on SWAG

COMMON SENSE REASONING LANGUAGE MODELLING LEXICAL SIMPLIFICATION LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE QUESTION ANSWERING READING COMPREHENSION SENTIMENT ANALYSIS

44,554

Paper
Code

XLNet: Generalized Autoregressive Pretraining for Language Understanding

NeurIPS 2019 • huggingface/transformers •

With the capability of modeling bidirectional contexts, denoising autoencoding based pretraining like BERT achieves better performance than pretraining approaches based on autoregressive language modeling.

Ranked #1 on Sentiment Analysis on Yelp Fine-grained classification

DOCUMENT RANKING HUMOR DETECTION LANGUAGE MODELLING NATURAL LANGUAGE INFERENCE PARAPHRASE IDENTIFICATION QUESTION ANSWERING READING COMPREHENSION SENTIMENT ANALYSIS TEXT CLASSIFICATION

44,554

Paper
Code

SqueezeBERT: What can computer vision teach NLP about efficient neural networks?

19 Jun 2020 • huggingface/transformers •

Humans read and write hundreds of billions of messages every day.

Ranked #15 on Natural Language Inference on RTE

LINGUISTIC ACCEPTABILITY NATURAL LANGUAGE INFERENCE SENTIMENT ANALYSIS TRANSFER LEARNING

36,726

Paper
Code

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

ICLR 2020 • google-research/bert •

Recent developments in natural language representations have been accompanied by large and expensive models that leverage vast amounts of general-domain text through self-supervised pre-training.

KNOWLEDGE DISTILLATION LANGUAGE MODELLING MODEL COMPRESSION SENTIMENT ANALYSIS

27,655

Paper
Code