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 28512900 of 5630 papers

TitleStatusHype
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical AttentionCode0
Enhancing Pharmacovigilance with Drug Reviews and Social MediaCode0
How recurrent networks implement contextual processing in sentiment analysisCode1
Sentiment Analysis of Arabic Algerian Dialect Using a Supervised MethodCode1
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation0
Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and ModelsCode0
Multi-source Attention for Unsupervised Domain AdaptationCode1
Jointly Modeling Aspect and Sentiment with Dynamic Heterogeneous Graph Neural NetworksCode1
Weight Poisoning Attacks on Pre-trained ModelsCode1
Classification Benchmarks for Under-resourced Bengali Language based on Multichannel Convolutional-LSTM NetworkCode1
DeepSentiPers: Novel Deep Learning Models Trained Over Proposed Augmented Persian Sentiment CorpusCode1
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis:Findings of A 20 Domain Study0
Pruning and Sparsemax Methods for Hierarchical Attention NetworksCode0
Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic0
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing0
Teacher-Class Network: A Neural Network Compression MechanismCode0
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation0
Enhancing Review Comprehension with Domain-Specific Commonsense0
Deep Learning Based Text Classification: A Comprehensive ReviewCode1
Predicting Strategic Behavior from Free TextCode0
Evaluating Models' Local Decision Boundaries via Contrast SetsCode1
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment AnalysisCode0
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment AnalysisCode1
Give your Text Representation Models some Love: the Case for BasqueCode0
Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow0
Noisy Text Data: Achilles' Heel of BERT0
Semantic Enrichment of Nigerian Pidgin English for Contextual Sentiment Classification0
Heavy-tailed Representations, Text Polarity Classification & Data Augmentation0
Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream TasksCode0
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than GeneratorsCode1
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network ApproachCode0
Sentiment Analysis in Drug Reviews using Supervised Machine Learning Algorithms0
A Novel Twitter Sentiment Analysis Model with Baseline Correlation for Financial Market Prediction with Improved Efficiency0
Cost-Sensitive BERT for Generalisable Sentence Classification with Imbalanced DataCode1
Learning to Encode Position for Transformer with Continuous Dynamical ModelCode1
TF-IDFC-RF: A Novel Supervised Term Weighting Scheme0
Sentiment Analysis with Contextual Embeddings and Self-Attention0
A Precisely Xtreme-Multi Channel Hybrid Approach For Roman Urdu Sentiment Analysis0
Adaptive Name Entity Recognition under Highly Unbalanced Data0
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models0
A Multi-Source Entity-Level Sentiment Corpus for the Financial Domain: The FinLin Corpus0
Quality of Word Embeddings on Sentiment Analysis Tasks0
SeMemNN: A Semantic Matrix-Based Memory Neural Network for Text ClassificationCode0
Word Sense Disambiguation: A comprehensive knowledge exploitation framework0
What Emotions Make One or Five Stars? Understanding Ratings of Online Product Reviews by Sentiment Analysis and XAI0
AraBERT: Transformer-based Model for Arabic Language UnderstandingCode1
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT0
Investigating Typed Syntactic Dependencies for Targeted Sentiment Classification Using Graph Attention Neural NetworkCode1
KryptoOracle: A Real-Time Cryptocurrency Price Prediction Platform Using Twitter SentimentsCode1
Gated Mechanism for Attention Based Multimodal 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