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 13511400 of 5630 papers

TitleStatusHype
Multilingual Auxiliary Tasks Training: Bridging the Gap between Languages for Zero-Shot Transfer of Hate Speech Detection ModelsCode0
Entity-level Sentiment Analysis in Contact Center Telephone Conversations0
Volatility forecasting using Deep Learning and sentiment analysis0
A Benchmark Study of Contrastive Learning for Arabic Social MeaningCode0
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data AugmentationCode0
TCAB: A Large-Scale Text Classification Attack BenchmarkCode0
Robustifying Sentiment Classification by Maximally Exploiting Few CounterfactualsCode0
Design a Sustainable Micro-mobility Future: Trends and Challenges in the United States and European Union Using Natural Language Processing Techniques0
Machine and Deep Learning Methods with Manual and Automatic Labelling for News Classification in Bangla Language0
Improving Aspect Sentiment Quad Prediction via Template-Order Data AugmentationCode1
Large Discourse Treebanks from Scalable Distant Supervision0
Fine-mixing: Mitigating Backdoors in Fine-tuned Language ModelsCode8
Sentiment-Aware Word and Sentence Level Pre-training for Sentiment AnalysisCode1
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge GraphsCode4
NADI 2022: The Third Nuanced Arabic Dialect Identification Shared TaskCode0
Fine-tuned Sentiment Analysis of COVID-19 Vaccine-Related Social Media Data: Comparative Study0
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated LearningCode0
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual ExplanationsCode0
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice PerspectiveCode4
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor AttacksCode1
Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons0
Best Practices in the Creation and Use of Emotion Lexicons0
Rethinking Annotation: Can Language Learners Contribute?0
Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based MethodsCode0
Ensemble Creation via Anchored Regularization for Unsupervised Aspect Extraction0
On the Evaluation of the Plausibility and Faithfulness of Sentiment Analysis Explanations0
Instruction Tuning for Few-Shot Aspect-Based Sentiment AnalysisCode1
Transformer-based Text Classification on Unified Bangla Multi-class Emotion CorpusCode0
Transfer Learning with Joint Fine-Tuning for Multimodal Sentiment AnalysisCode1
CORE: A Retrieve-then-Edit Framework for Counterfactual Data GenerationCode0
Label-Driven Denoising Framework for Multi-Label Few-Shot Aspect Category DetectionCode1
Missing Modality meets Meta Sampling (M3S): An Efficient Universal Approach for Multimodal Sentiment Analysis with Missing Modality0
PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection0
BootAug: Boosting Text Augmentation via Hybrid Instance Filtering FrameworkCode1
Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese TextsCode0
Explaining Patterns in Data with Language Models via Interpretable AutopromptingCode1
Sentiment Analysis of ESG disclosures on Stock Market0
SSP-Based Construction of Evaluation-Annotated Data for Fine-Grained Aspect-Based Sentiment Analysis0
Automatic Identification of 5C Vaccine Behaviour on Social Media0
Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification0
Analyzing Coreference and Bridging in Product Reviews0
Fraunhofer SIT@SMM4H’22: Learning to Predict Stances and Premises in Tweets related to COVID-19 Health Orders Using Generative Models0
Span Extraction Aided Improved Code-mixed Sentiment ClassificationCode0
BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset0
Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments0
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment ClassificationCode0
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context FeaturesCode0
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis0
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis0
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance0
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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