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 37013750 of 5630 papers

TitleStatusHype
THU\_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM0
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning ProblemCode0
Neural Tensor Networks with Diagonal Slice Matrices0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis0
RiskFinder: A Sentence-level Risk Detector for Financial Reports0
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets0
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification0
The Importance of Calibration for Estimating Proportions from Annotations0
Is Something Better than Nothing? Automatically Predicting Stance-based Arguments Using Deep Learning and Small Labelled Dataset0
NLPZZX at SemEval-2018 Task 1: Using Ensemble Method for Emotion and Sentiment Intensity Determination0
YNU-HPCC at SemEval-2018 Task 1: BiLSTM with Attention based Sentiment Analysis for Affect in Tweets0
CENTEMENT at SemEval-2018 Task 1: Classification of Tweets using Multiple Thresholds with Self-correction and Weighted Conditional Probabilities0
Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment0
SystemT: Declarative Text Understanding for Enterprise0
Learning Domain Representation for Multi-Domain Sentiment Classification0
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities0
DeepMiner at SemEval-2018 Task 1: Emotion Intensity Recognition Using Deep Representation Learning0
T\"ubingen-Oslo at SemEval-2018 Task 2: SVMs perform better than RNNs in Emoji Prediction0
Modeling Inter-Aspect Dependencies for Aspect-Based Sentiment AnalysisCode0
Solving Data Sparsity for Aspect Based Sentiment Analysis Using Cross-Linguality and Multi-Linguality0
DebugSL: An Interactive Tool for Debugging Sentiment LexiconsCode0
TCS Research at SemEval-2018 Task 1: Learning Robust Representations using Multi-Attention Architecture0
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach0
Efficient Low-rank Multimodal Fusion with Modality-Specific FactorsCode0
Anaphora and Coreference Resolution: A Review0
A Corpus of English-Hindi Code-Mixed Tweets for Sarcasm DetectionCode0
How Important Is a Neuron?Code0
Convolutional neural network compression for natural language processing0
Multimodal Sentiment Analysis To Explore the Structure of EmotionsCode0
A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter0
Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling MechanismsCode0
Bilingual Sentiment Embeddings: Joint Projection of Sentiment Across LanguagesCode0
Sentiment Analysis of Arabic Tweets: Feature Engineering and A Hybrid Approach0
Aff2Vec: Affect--Enriched Distributional Word Representations0
Improving Aspect Term Extraction with Bidirectional Dependency Tree RepresentationCode0
Model Aggregation via Good-Enough Model Spaces0
Knowledge-enriched Two-layered Attention Network for Sentiment Analysis0
Diverse Few-Shot Text Classification with Multiple MetricsCode0
Aspect Based Sentiment Analysis with Gated Convolutional NetworksCode0
Improved Sentence Modeling using Suffix Bidirectional LSTM0
What's in a Domain? Learning Domain-Robust Text Representations using Adversarial TrainingCode0
A La Carte Embedding: Cheap but Effective Induction of Semantic Feature VectorsCode0
Backpropagating through Structured Argmax using a SPIGOTCode0
Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems0
Domain Adapted Word Embeddings for Improved Sentiment ClassificationCode0
Double Embeddings and CNN-based Sequence Labeling for Aspect ExtractionCode0
Learning Domain-Sensitive and Sentiment-Aware Word Embeddings0
Joint Embedding of Words and Labels for Text ClassificationCode0
Statistical Analysis on E-Commerce Reviews, with Sentiment Classification using Bidirectional Recurrent Neural Network (RNN)Code0
Sentence-State LSTM for Text RepresentationCode0
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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