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 18511900 of 5630 papers

TitleStatusHype
Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma DistributionsCode1
Explainable Sentence-Level Sentiment Analysis for Amazon Product Reviews0
Which is Making the Contribution: Modulating Unimodal and Cross-modal Dynamics for Multimodal Sentiment Analysis0
Prune Once for All: Sparse Pre-Trained Language Models0
A Computational Approach to Walt Whitman's Stylistic Changes in Leaves of Grass0
Human-in-the-Loop Disinformation Detection: Stance, Sentiment, or Something Else?0
MNet-Sim: A Multi-layered Semantic Similarity Network to Evaluate Sentence Similarity0
Patent Sentiment Analysis to Highlight Patent ParagraphsCode1
TaskDrop: A Competitive Baseline for Continual Learning of Sentiment ClassificationCode0
Leveraging Sentiment Analysis Knowledge to Solve Emotion Detection Tasks0
An Empirical Study of the Effectiveness of an Ensemble of Stand-alone Sentiment Detection Tools for Software Engineering DatasetsCode0
A Case Study and Qualitative Analysis of Simple Cross-Lingual Opinion MiningCode0
Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-TrainingCode1
End-to-End Annotator Bias Approximation on Crowdsourced Single-Label Sentiment AnalysisCode0
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density0
Span Detection for Vietnamese Aspect-Based Sentiment Analysis0
Stronger Baseline for Robust Results in Multimodal Sentiment Analysis0
A Sentiment Analysis of Men’s and Women’s Speech in the BNC640
Domain and Task-Informed Sample Selection for Cross-Domain Target-based Sentiment Analysis0
What Models Know About Their Attackers: Deriving Attacker Information From Latent Representations0
“Something Something Hota Hai!” An Explainable Approach towards Sentiment Analysis on Indian Code-Mixed Data0
A Mixed-Methods Analysis of Western and Hong Kong–based Reporting on the 2019–2020 Protests0
A Fine-Grained Annotated Corpus for Target-Based Opinion Analysis of Economic and Financial Narratives0
Corporate Bankruptcy Prediction with BERT Model0
Using Word Embedding to Reveal Monetary Policy Explanation Changes0
NB-MLM: Efficient Domain Adaptation of Masked Language Models for Sentiment AnalysisCode0
FAST: Fast Annotation tool for SmarT devicesCode1
Cross-lingual Aspect-based Sentiment Analysis with Aspect Term Code-SwitchingCode1
Multimodal Phased Transformer for Sentiment AnalysisCode1
Beta Distribution Guided Aspect-aware Graph for Aspect Category Sentiment Analysis with Affective KnowledgeCode1
Implicit Sentiment Analysis with Event-centered Text Representation0
Syntactically-Informed Unsupervised Paraphrasing with Non-Parallel DataCode0
Semantics-Preserved Data Augmentation for Aspect-Based Sentiment Analysis0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories0
The Effect of Round-Trip Translation on Fairness in Sentiment Analysis0
Incorporating medical knowledge in BERT for clinical relation extraction0
Joint Multi-modal Aspect-Sentiment Analysis with Auxiliary Cross-modal Relation DetectionCode1
Continual Few-Shot Learning for Text ClassificationCode0
Reinforced Counterfactual Data Augmentation for Dual Sentiment ClassificationCode0
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading ComprehensionCode0
Saliency-based Multi-View Mixed Language Training for Zero-shot Cross-lingual Classification0
SentNoB: A Dataset for Analysing Sentiment on Noisy Bangla TextsCode0
Aspect-based Sentiment Analysis in Question Answering ForumsCode0
Competing Independent Modules for Knowledge Integration and Optimization0
Casting the Same Sentiment Classification ProblemCode0
Effectively Leveraging BERT for Legal Document Classification0
A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives0
FinEAS: Financial Embedding Analysis of SentimentCode1
Classifying YouTube Comments Based on Sentiment and Type of SentenceCode0
AdvCodeMix: Adversarial Attack on Code-Mixed Data0
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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