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 11011150 of 5630 papers

TitleStatusHype
RuSentNE-2023: Evaluating Entity-Oriented Sentiment Analysis on Russian News TextsCode1
Targeted Data Generation: Finding and Fixing Model Weaknesses0
Characterizing and Measuring Linguistic Dataset DriftCode0
Mapping ChatGPT in Mainstream Media to Unravel Jobs and Diversity Challenges: Early Quantitative Insights through Sentiment Analysis and Word Frequency Analysis0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text0
Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data0
Zero-shot Approach to Overcome Perturbation Sensitivity of PromptsCode0
Neural Natural Language Processing for Long Texts: A Survey on Classification and Summarization0
Denoising Bottleneck with Mutual Information Maximization for Video Multimodal FusionCode0
Exploring Sentiment Analysis Techniques in Natural Language Processing: A Comprehensive Review0
A Survey of Diffusion Models in Natural Language Processing0
Sentiment Analysis Using Aligned Word Embeddings for Uralic Languages0
Sentiment Analysis in the Era of Large Language Models: A Reality CheckCode1
Prompting Large Language Models for Counterfactual Generation: An Empirical Study0
OverPrompt: Enhancing ChatGPT through Efficient In-Context LearningCode0
Embrace Opportunities and Face Challenges: Using ChatGPT in Undergraduate Students' Collaborative Interdisciplinary Learning0
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet ExtractionCode0
Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited QuestionsCode0
Having Beer after Prayer? Measuring Cultural Bias in Large Language ModelsCode1
PIEClass: Weakly-Supervised Text Classification with Prompting and Noise-Robust Iterative Ensemble TrainingCode1
Cross-Attention is Not Enough: Incongruity-Aware Dynamic Hierarchical Fusion for Multimodal Affect RecognitionCode0
When Does Aggregating Multiple Skills with Multi-Task Learning Work? A Case Study in Financial NLPCode0
Multilingual Large Language Models Are Not (Yet) Code-Switchers0
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals0
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis0
Cross-functional Analysis of Generalisation in Behavioural LearningCode0
MvP: Multi-view Prompting Improves Aspect Sentiment Tuple PredictionCode1
SEntFiN 1.0: Entity-Aware Sentiment Analysis for Financial News0
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment0
Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages0
Zero-Shot Text Classification via Self-Supervised TuningCode1
Support for Stock Trend Prediction Using Transformers and Sentiment Analysis0
Reasoning Implicit Sentiment with Chain-of-Thought PromptingCode1
Comparing Biases and the Impact of Multilingual Training across Multiple Languages0
NollySenti: Leveraging Transfer Learning and Machine Translation for Nigerian Movie Sentiment ClassificationCode0
UniEX: An Effective and Efficient Framework for Unified Information Extraction via a Span-extractive Perspective0
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment AnalysisCode1
Executive Voiced Laughter and Social Approval: An Explorative Machine Learning Study0
The Weighted Möbius Score: A Unified Framework for Feature AttributionCode0
Shared and Private Information Learning in Multimodal Sentiment Analysis with Deep Modal Alignment and Self-supervised Multi-Task Learning0
Asymmetric feature interaction for interpreting model predictionsCode0
Multimodal Sentiment Analysis: A Survey0
BanglaBook: A Large-scale Bangla Dataset for Sentiment Analysis from Book ReviewsCode1
COCKATIEL: COntinuous Concept ranKed ATtribution with Interpretable ELements for explaining neural net classifiers on NLP tasksCode0
CrudeBERT: Applying Economic Theory towards fine-tuning Transformer-based Sentiment Analysis Models to the Crude Oil Market0
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks0
Structured Sentiment Analysis as Transition-based Dependency Parsing0
Interpretable multimodal sentiment analysis based on textual modality descriptions by using large-scale language modelsCode0
The MuSe 2023 Multimodal Sentiment Analysis Challenge: Mimicked Emotions, Cross-Cultural Humour, and PersonalisationCode1
Natural Language Processing and Sentiment Analysis on Bangla Social Media Comments on Russia–Ukraine War Using TransformersCode0
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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