SOTAVerified

Sentiment Analysis

Sentiment Analysis is the task of classifying the polarity of a given text. For instance, a text-based tweet can be categorized into either "positive", "negative", or "neutral". Given the text and accompanying labels, a model can be trained to predict the correct sentiment.

Sentiment Analysis techniques can be categorized into machine learning approaches, lexicon-based approaches, and even hybrid methods. Some subcategories of research in sentiment analysis include: multimodal sentiment analysis, aspect-based sentiment analysis, fine-grained opinion analysis, language specific sentiment analysis.

More recently, deep learning techniques, such as RoBERTa and T5, are used to train high-performing sentiment classifiers that are evaluated using metrics like F1, recall, and precision. To evaluate sentiment analysis systems, benchmark datasets like SST, GLUE, and IMDB movie reviews are used.

Further readings:

Papers

Showing 31013150 of 5630 papers

TitleStatusHype
Monitoring stance towards vaccination in Twitter messages0
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment AnalysisCode0
Can Sentiment Analysis Reveal Structure in a Plotless Novel?0
NEZHA: Neural Contextualized Representation for Chinese Language UnderstandingCode0
Learning with Noisy Labels for Sentence-level Sentiment Classification0
Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions0
Single Training Dimension Selection for Word Embedding with PCA0
Sequential Learning of Convolutional Features for Effective Text Classification0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
A Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer0
Learning Latent Parameters without Human Response Patterns: Item Response Theory with Artificial CrowdsCode1
FinBERT: Financial Sentiment Analysis with Pre-trained Language ModelsCode0
uniblock: Scoring and Filtering Corpus with Unicode Block InformationCode0
Non-local Recurrent Neural Memory for Supervised Sequence Modeling0
Rethinking Attribute Representation and Injection for Sentiment ClassificationCode0
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification0
Well-Read Students Learn Better: On the Importance of Pre-training Compact ModelsCode2
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics0
Fine-grained Sentiment Analysis with Faithful Attention0
TDAM: a Topic-Dependent Attention Model for Sentiment Analysis0
Shallow Domain Adaptive Embeddings for Sentiment Analysis0
Integrating Multimodal Information in Large Pretrained TransformersCode0
Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding0
Variational Fusion for Multimodal Sentiment Analysis0
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction0
A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text ClassificationCode0
Sentiment Analysis of Typhoon Related Tweets using Standard and Bidirectional Recurrent Neural Networks0
Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet level using Deep Learning0
Lexicon Guided Attentive Neural Network Model for Argument Mining0
Mazajak: An Online Arabic Sentiment Analyser0
Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects0
hULMonA: The Universal Language Model in ArabicCode0
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning0
Equity Beyond Bias in Language Technologies for Education0
Confirming the Non-compositionality of Idioms for Sentiment Analysis0
Adversarial Attack on Sentiment Classification0
Improving Sentiment Classification in Slovak LanguageCode0
Sentiment Analysis Model for Opinionated Awngi Text: Case of Music Reviews0
Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models0
Data Set for Stance and Sentiment Analysis from User Comments on Croatian News0
Sentiment Analysis for Multilingual CorporaCode0
Confirmatory Aspect-based Opinion Mining Processes0
Airbnb Price Prediction Using Machine Learning and Sentiment AnalysisCode0
ERNIE 2.0: A Continual Pre-training Framework for Language UnderstandingCode3
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets0
Fusing location and text features for sentiment classification0
Pars-ABSA: an Aspect-based Sentiment Analysis dataset for PersianCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2MT-DNN-SMARTAccuracy97.5Unverified
3T5-11BAccuracy97.5Unverified
4MUPPET Roberta LargeAccuracy97.4Unverified
5T5-3BAccuracy97.4Unverified
6ALBERTAccuracy97.1Unverified
7StructBERTRoBERTa ensembleAccuracy97.1Unverified
8XLNet (single model)Accuracy97Unverified
9SMARTRoBERTaDev Accuracy96.9Unverified
10ELECTRAAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68Unverified
2RoBERTa-largeAccuracy96.54Unverified
3XLNetAccuracy96.21Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1Unverified
6GraphStarAccuracy96Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9Unverified
10BERT large finetune UDAAccuracy95.8Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07Unverified
6Heinsen Routing + GPT-2Accuracy58.5Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2Unverified
8BERT LargeAccuracy55.5Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9Unverified
10BCN+ELMoAccuracy54.7Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88Unverified
2SVDCNNError4.74Unverified
3LEAMError4.69Unverified
4fastText, h=10, bigramError4.3Unverified
5SWEM-hierError4.19Unverified
6SRNNError3.96Unverified
7M-ACNNError3.89Unverified
8DNC+CUWError3.6Unverified
9CCCapsNetError3.52Unverified
10Block-sparse LSTMError3.27Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500Unverified
2VLAWEAccuracy93.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5Unverified
4AnglE-LLaMA-7BAccuracy91.09Unverified
5byte mLSTM7Accuracy86.8Unverified
6MEANAccuracy84.5Unverified
7RNN-CapsuleAccuracy83.8Unverified
8Capsule-BAccuracy82.3Unverified
9SuBiLSTM-TiedAccuracy81.6Unverified
10USE_T+CNNAccuracy81.59Unverified