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 20512100 of 5630 papers

TitleStatusHype
Integrated Directional Gradients: Feature Interaction Attribution for Neural NLP ModelsCode0
Rethinking Stealthiness of Backdoor Attack against NLP ModelsCode1
eMLM: A New Pre-training Objective for Emotion Related TasksCode1
Dual Graph Convolutional Networks for Aspect-based Sentiment AnalysisCode1
CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion NetworkCode1
Aspect-Category-Opinion-Sentiment Quadruple Extraction with Implicit Aspects and OpinionsCode1
UMUTeam at SemEval-2021 Task 7: Detecting and Rating Humor and Offense with Linguistic Features and Word EmbeddingsCode0
How do different factors Impact the Inter-language Similarity? A Case Study on Indian languages0
Towards Generative Aspect-Based Sentiment AnalysisCode1
Gates Are Not What You Need in RNNsCode0
Geolocation differences of language use in urban areas0
Transformer-Encoder-GRU (T-E-GRU) for Chinese Sentiment Analysis on Chinese Comment Text0
Opinion Prediction with User FingerprintingCode0
Sentiment Analysis of the COVID-related r/Depression Posts0
Bi-Bimodal Modality Fusion for Correlation-Controlled Multimodal Sentiment AnalysisCode1
Towards Robustness Against Natural Language Word SubstitutionsCode1
Arabic aspect sentiment polarity classification using BERT0
Preliminary Steps Towards Federated Sentiment Classification0
Compensation Learning0
A Study on Herd Behavior Using Sentiment Analysis in Online Social Network0
MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation ToolboxCode1
Negation Handling in Machine Learning-Based Sentiment Classification for Colloquial Arabic0
Impacts Towards a comprehensive assessment of the book impact by integrating multiple evaluation sources0
Spinning Sequence-to-Sequence Models with Meta-Backdoors0
Out of the Shadows: Analyzing Anonymous' Twitter Resurgence during the 2020 Black Lives Matter Protests0
The Effectiveness of Intermediate-Task Training for Code-Switched Natural Language Understanding0
Aspect-based Sentiment Analysis using BERT with Disentangled AttentionCode1
Stock price prediction using BERT and GAN0
M2Lens: Visualizing and Explaining Multimodal Models for Sentiment Analysis0
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews0
A Robust Deep Ensemble Classifier for Figurative Language Detection0
Fuzzy-Rough Nearest Neighbour Approaches for Emotion Detection in TweetsCode0
Transfer Learning for Improving Results on Russian Sentiment DatasetsCode0
Identifying negativity factors from social media text corpus using sentiment analysis method0
Sarcasm Detection: A Comparative Study0
Domain Adaptation for Sentiment Analysis Using Increased Intraclass Separation0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments0
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language UnderstandingCode1
Deep Transfer Learning Baselines for Sentiment Analysis in RussianCode1
Cross-lingual alignments of ELMo contextual embeddings0
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment AnalysisCode1
Sentiment analysis for Urdu online reviews using deep learning modelsCode0
Current Landscape of the Russian Sentiment Corpora0
Transfer-based adaptive tree for multimodal sentiment analysis based on user latent aspects0
Closed-form Continuous-time Neural ModelsCode2
Deep Multi-Task Model for Sarcasm Detection and Sentiment Analysis in Arabic Language0
Classifying Textual Data with Pre-trained Vision Models through Transfer Learning and Data TransformationsCode0
Charformer: Fast Character Transformers via Gradient-based Subword TokenizationCode1
Sequential Late Fusion Technique for Multi-modal Sentiment Analysis0
Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis0
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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