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 24012450 of 5630 papers

TitleStatusHype
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion0
Sentiment Analysis for Emotional Speech Synthesis in a News Dialogue System0
Few-Shot Text Classification with Edge-Labeling Graph Neural Network-Based Prototypical Network0
Word-Level Uncertainty Estimation for Black-Box Text Classifiers using RNNsCode0
Building Large-Scale English and Korean Datasets for Aspect-Level Sentiment Analysis in Automotive DomainCode0
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts0
Effective Few-Shot Classification with Transfer Learning0
SWAFN: Sentimental Words Aware Fusion Network for Multimodal Sentiment AnalysisCode1
Bayes-enhanced Lifelong Attention Networks for Sentiment Classification0
Jointly Learning Aspect-Focused and Inter-Aspect Relations with Graph Convolutional Networks for Aspect Sentiment AnalysisCode1
Syntactically Aware Cross-Domain Aspect and Opinion Terms Extraction0
Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions0
Aspect-Category based Sentiment Analysis with Hierarchical Graph Convolutional Network0
Constituency Lattice Encoding for Aspect Term ExtractionCode0
Multimodal Review Generation with Privacy and Fairness AwarenessCode0
Label Correction Model for Aspect-based Sentiment Analysis0
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment AnalysisCode1
METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis0
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification0
Financial Sentiment Analysis: An Investigation into Common Mistakes and Silver Bullets0
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional NetworksCode1
Arabizi Language Models for Sentiment Analysis0
Exploring Amharic Sentiment Analysis from Social Media Texts: Building Annotation Tools and Classification Models0
Unsupervised Aspect-Level Sentiment Controllable Style Transfer0
Multi-task Learning for Automated Essay Scoring with Sentiment Analysis0
Aspect Extraction Using Coreference Resolution and Unsupervised Filtering0
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes0
GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment AnalysisCode1
Graph Attention Network with Memory Fusion for Aspect-level Sentiment AnalysisCode1
Resource Creation and Evaluation of Aspect Based Sentiment Analysis in Urdu0
PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis0
Memebusters at SemEval-2020 Task 8: Feature Fusion Model for Sentiment Analysis on Memes Using Transfer LearningCode0
BERT at SemEval-2020 Task 8: Using BERT to Analyse Meme Emotions0
BennettNLP at SemEval-2020 Task 8: Multimodal sentiment classification Using Hybrid Hierarchical Classifier0
MSR India at SemEval-2020 Task 9: Multilingual Models Can Do Code-Mixing Too0
XLP at SemEval-2020 Task 9: Cross-lingual Models with Focal Loss for Sentiment Analysis of Code-Mixing Language0
JUNLP at SemEval-2020 Task 9: Sentiment Analysis of Hindi-English Code Mixed Data Using Grid Search Cross Validation0
IRLab\_DAIICT at SemEval-2020 Task 9: Machine Learning and Deep Learning Methods for Sentiment Analysis of Code-Mixed Tweets0
LIMSI\_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis0
Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models0
HinglishNLP at SemEval-2020 Task 9: Fine-tuned Language Models for Hinglish Sentiment DetectionCode1
UI at SemEval-2020 Task 8: Text-Image Fusion for Sentiment Classification0
SIS@IIITH at SemEval-2020 Task 8: An Overview of Simple Text Classification Methods for Meme Analysis0
UoR at SemEval-2020 Task 8: Gaussian Mixture Modelling (GMM) Based Sampling Approach for Multi-modal Memotion Analysis0
Will\_go at SemEval-2020 Task 9: An Accurate Approach for Sentiment Analysis on Hindi-English Tweets Based on Bert and Pesudo Label Strategy0
SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes0
IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text0
Urszula Wali\'nska at SemEval-2020 Task 8: Fusion of Text and Image Features Using LSTM and VGG16 for Memotion Analysis0
FII-UAIC at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text Using CNN0
Ferryman at SemEval-2020 Task 7: Ensemble Model for Assessing Humor in Edited News Headlines0
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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