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 501525 of 5630 papers

TitleStatusHype
Exchanging-based Multimodal Fusion with TransformerCode1
Who Blames or Endorses Whom? Entity-to-Entity Directed Sentiment Extraction in News TextCode1
Expose Backdoors on the Way: A Feature-Based Efficient Defense against Textual Backdoor AttacksCode1
YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain ReviewsCode1
Enhanced Aspect-Based Sentiment Analysis Models with Progressive Self-supervised Attention LearningCode1
ZEN: Pre-training Chinese Text Encoder Enhanced by N-gram RepresentationsCode1
Zero-Shot Text Classification via Self-Supervised TuningCode1
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet ExtractionCode1
Exploiting BERT For Multimodal Target Sentiment Classification Through Input Space TranslationCode1
Enhancing Aspect-level Sentiment Analysis with Word DependenciesCode1
GRUBERT: A GRU-Based Method to Fuse BERT Hidden Layers for Twitter Sentiment AnalysisCode1
Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional NetworksCode1
Nyströmformer: A Nyström-Based Algorithm for Approximating Self-AttentionCode1
A Robustly Optimized BMRC for Aspect Sentiment Triplet ExtractionCode1
A semantically enhanced dual encoder for aspect sentiment triplet extractionCode1
A Challenge Dataset and Effective Models for Aspect-Based Sentiment AnalysisCode0
Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset RepositoryCode0
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccinesCode0
Enhancing Affinity Propagation for Improved Public Sentiment InsightsCode0
Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment AnalysisCode0
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical AttentionCode0
Applying QNLP to sentiment analysis in financeCode0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural ModelsCode0
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment AnalysisCode0
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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