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 14511500 of 5630 papers

TitleStatusHype
DIBERT: Dependency Injected Bidirectional Encoder Representations from TransformersCode0
Differential Privacy Has Disparate Impact on Model AccuracyCode0
A Syntax-Injected Approach for Faster and More Accurate Sentiment AnalysisCode0
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and ClassificationCode0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and DocumentsCode0
Detection of Adversarial Examples in NLP: Benchmark and Baseline via Robust Density EstimationCode0
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful TechniquesCode0
Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density EstimationCode0
Data Selection Strategies for Multi-Domain Sentiment AnalysisCode0
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment AnalysisCode0
Delta-training: Simple Semi-Supervised Text Classification using Pretrained Word EmbeddingsCode0
Deep Unordered Composition Rivals Syntactic Methods for Text ClassificationCode0
Defense of Word-level Adversarial Attacks via Random Substitution EncodingCode0
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal FusionCode0
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density EstimationCode0
Analyzing Language Bias Between French and English in Conventional Multilingual Sentiment Analysis ModelsCode0
PELESent: Cross-domain polarity classification using distant supervisionCode0
DataStories at SemEval-2017 Task 4: Deep LSTM with Attention for Message-level and Topic-based Sentiment AnalysisCode0
Deep Neural Networks for Bot DetectionCode0
Deep Pyramid Convolutional Neural Networks for Text CategorizationCode0
Deep Learning for Sentiment Analysis : A SurveyCode0
Deep Learning with Eigenvalue Decay RegularizerCode0
Deep Learning Brasil at ABSAPT 2022: Portuguese Transformer Ensemble ApproachesCode0
Buzz to Broadcast: Predicting Sports Viewership Using Social Media EngagementCode0
BLIND: Bias Removal With No DemographicsCode0
DebugSL: An Interactive Tool for Debugging Sentiment LexiconsCode0
Predicting Strategic Behavior from Free TextCode0
Deep Learning for Hate Speech Detection in TweetsCode0
Deep Learning-Based Sentiment Analysis of COVID-19 Vaccination Responses from Twitter DataCode0
Low-Resource Language Processing: An OCR-Driven Summarization and Translation PipelineCode0
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural TherapyCode0
Detection of Word Adversarial Examples in NLP: Benchmark and Baseline via Robust Density EstimationCode0
Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker ContainerCode0
Progressive Sentiment Analysis for Code-Switched Text DataCode0
bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis0
Building Web-Interfaces for Vector Semantic Models with the WebVectors Toolkit0
A Pretrained YouTuber Embeddings for Improving Sentiment Classification of YouTube Comments0
Building Sentiment Lexicons for Mainland Scandinavian Languages Using Machine Translation and Sentence Embeddings0
Building Sentiment Lexicons for All Major Languages0
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis0
Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach0
Approximate Conditional Coverage & Calibration via Neural Model Approximations0
Approaches for Sentiment Analysis on Twitter: A State-of-Art study0
A Hybrid Approach To Aspect Based Sentiment Analysis Using Transfer Learning0
Building Chinese Affective Resources in Valence-Arousal Dimensions0
Building a SentiWordNet for Odia0
Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection0
Building a Pilot Software Quality-in-Use Benchmark Dataset0
Building and Modelling Multilingual Subjective Corpora0
Show:102550
← PrevPage 30 of 113Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2T5-11BAccuracy97.5Unverified
3MT-DNN-SMARTAccuracy97.5Unverified
4T5-3BAccuracy97.4Unverified
5MUPPET Roberta LargeAccuracy97.4Unverified
6StructBERTRoBERTa ensembleAccuracy97.1Unverified
7ALBERTAccuracy97.1Unverified
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