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 10511100 of 5630 papers

TitleStatusHype
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment AnalysisCode0
A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla TextsCode0
A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text ClassificationCode0
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag RepresentationsCode0
A Simple Approach to Multilingual Polarity Classification in TwitterCode0
A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text ClassificationCode0
A Dataset and Strong Baselines for Classification of Czech News TextsCode0
DragonVerseQA: Open-Domain Long-Form Context-Aware Question-AnsweringCode0
Aligning Multilingual Embeddings for Improved Code-switched Natural Language UnderstandingCode0
Don't Count, Predict! An Automatic Approach to Learning Sentiment Lexicons for Short TextCode0
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?Code0
A SentiWordNet Strategy for Curriculum Learning in Sentiment AnalysisCode0
Domain-Expanded ASTE: Rethinking Generalization in Aspect Sentiment Triplet ExtractionCode0
Domain-Specific Language Model Post-Training for Indonesian Financial NLPCode0
Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionCode0
Domain Adapted Word Embeddings for Improved Sentiment ClassificationCode0
Domain Adaptation from ScratchCode0
Does Transliteration Help Multilingual Language Modeling?Code0
Domain Adaptation for Arabic Cross-Domain and Cross-Dialect Sentiment Analysis from Contextualized Word EmbeddingCode0
Domain Adversarial Fine-Tuning as an Effective RegularizerCode0
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-ExtractionCode0
A Sentiment Analysis Dataset for Code-Mixed Malayalam-EnglishCode0
Does It Make Sense to Explain a Black Box With Another Black Box?Code0
Document Embedding with Paragraph VectorsCode0
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall RatingsCode0
Does local pruning offer task-specific models to learn effectively ?Code0
Domain-Adversarial Neural NetworksCode0
Diving Deep into Sentiment: Understanding Fine-tuned CNNs for Visual Sentiment PredictionCode0
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility PredictionCode0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
Divide (Text) and Conquer (Sentiment): Improved Sentiment Classification by Constituent Conflict ResolutionCode0
Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker ContainerCode0
A Semantics-Based Measure of Emoji SimilarityCode0
Distributionally Robust Classifiers in Sentiment AnalysisCode0
AlbMoRe: A Corpus of Movie Reviews for Sentiment Analysis in AlbanianCode0
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble LearningCode0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Effective Use of Word Order for Text Categorization with Convolutional Neural NetworksCode0
Distilling Fine-grained Sentiment Understanding from Large Language ModelsCode0
Discrete Opinion Tree Induction for Aspect-based Sentiment AnalysisCode0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
Distilling neural networks into skipgram-level decision listsCode0
Disambiguation of Verbal ShiftersCode0
Differential Privacy Has Disparate Impact on Model AccuracyCode0
Adaptive Semi-supervised Learning for Cross-domain Sentiment ClassificationCode0
DINER: Debiasing Aspect-based Sentiment Analysis with Multi-variable Causal InferenceCode0
Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free ApproachCode0
Distilling Task-Specific Knowledge from BERT into Simple Neural NetworksCode0
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density EstimationCode0
Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density EstimationCode0
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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