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

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Showing 1301–1350 of 5630 papers

TitleStatusHype
Large Pre-Trained Models with Extra-Large Vocabularies: A Contrastive Analysis of Hebrew BERT Models and a New One to Outperform Them All—0
Sentiment analysis and opinion mining on E-commerce site—0
STAGE: Span Tagging and Greedy Inference Scheme for Aspect Sentiment Triplet ExtractionCode1
Label Alignment Regularization for Distribution ShiftCode0
A Survey of Text Representation Methods and Their Genealogy—0
A Machine Learning, Natural Language Processing Analysis of Youth Perspectives: Key Trends and Focus Areas for Sustainable Youth Development Policies—0
Deep Learning Training Procedure Augmentations—0
Using Selective Masking as a Bridge between Pre-training and Fine-tuning—0
Multitask Learning for Low Resource Spoken Language Understanding—0
Holistic Visual-Textual Sentiment Analysis with Prior ModelsCode0
Sarcasm Detection Framework Using Context, Emotion and Sentiment Features—0
TEMPERA: Test-Time Prompting via Reinforcement LearningCode1
UniMSE: Towards Unified Multimodal Sentiment Analysis and Emotion RecognitionCode2
LSTM based models stability in the context of Sentiment Analysis for social media—0
Detecting Conspiracy Theory Against COVID-19 Vaccines—0
An Algorithm for Routing Vectors in Sequences—0
UnifiedABSA: A Unified ABSA Framework Based on Multi-task Instruction Tuning—0
TensAIR: Real-Time Training of Neural Networks from Data-streamsCode0
Fast and Accurate FSA System Using ELBERT: An Efficient and Lightweight BERT—0
Generative Aspect-Based Sentiment Analysis with Contrastive Learning and Expressive StructureCode1
Language Agnostic Code-Mixing Data Augmentation by Predicting Linguistic Patterns—0
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences—0
Few-shot Multimodal Sentiment Analysis based on Multimodal Probabilistic Fusion PromptsCode1
MARLIN: Masked Autoencoder for facial video Representation LearnINgCode2
FinBERT-LSTM: Deep Learning based stock price prediction using News Sentiment Analysis—0
DiaASQ : A Benchmark of Conversational Aspect-based Sentiment Quadruple AnalysisCode1
BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis—0
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding—0
Towards Human-Centred Explainability Benchmarks For Text Classification—0
Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysis—0
Sentiment Analysis of Persian Language: Review of Algorithms, Approaches and Datasets—0
Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions—0
AX-MABSA: A Framework for Extremely Weakly Supervised Multi-label Aspect Based Sentiment AnalysisCode0
AfroLM: A Self-Active Learning-based Multilingual Pretrained Language Model for 23 African LanguagesCode1
A Multi-task Model for Sentiment Aided Stance Detection of Climate Change TweetsCode0
Fixing Model Bugs with Natural Language PatchesCode1
A Comparison of Automatic Labelling Approaches for Sentiment Analysis—0
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets—0
A Case Study of Chinese Sentiment Analysis on Social Media Reviews Based on LSTM—0
Multimodal Information Bottleneck: Learning Minimal Sufficient Unimodal and Multimodal RepresentationsCode1
Beyond Prompting: Making Pre-trained Language Models Better Zero-shot Learners by Clustering RepresentationsCode1
Sentiment Classification of Code-Switched Text using Pre-trained Multilingual Embeddings and Segmentation—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
On the Use of Modality-Specific Large-Scale Pre-Trained Encoders for Multimodal Sentiment AnalysisCode0
Retrieving Users' Opinions on Social Media with Multimodal Aspect-Based Sentiment AnalysisCode0
Exploring Robustness of Prefix Tuning in Noisy Data: A Case Study in Financial Sentiment Analysis—0
Leveraging Affirmative Interpretations from Negation Improves Natural Language UnderstandingCode0
Sinhala Sentence Embedding: A Two-Tiered Structure for Low-Resource Languages—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Progressive Sentiment Analysis for Code-Switched Text DataCode0
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All datasetsSST-2 Binary classificationIMDbSST-5 Fine-grained classificationYelp Binary classificationMRYelp Fine-grained classificationBanglaBookDynaSentSST-3Sentiment MergedUser and product informationAmazon Review Full

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100—Unverified
2MT-DNN-SMARTAccuracy97.5—Unverified
3T5-11BAccuracy97.5—Unverified
4MUPPET Roberta LargeAccuracy97.4—Unverified
5T5-3BAccuracy97.4—Unverified
6ALBERTAccuracy97.1—Unverified
7StructBERTRoBERTa ensembleAccuracy97.1—Unverified
8XLNet (single model)Accuracy97—Unverified
9SMARTRoBERTaDev Accuracy96.9—Unverified
10ELECTRAAccuracy96.9—Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68—Unverified
2RoBERTa-largeAccuracy96.54—Unverified
3XLNetAccuracy96.21—Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2—Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1—Unverified
6GraphStarAccuracy96—Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94—Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92—Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9—Unverified
10BERT large finetune UDAAccuracy95.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27—Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2—Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8—Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1—Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07—Unverified
6Heinsen Routing + GPT-2Accuracy58.5—Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2—Unverified
8BERT LargeAccuracy55.5—Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9—Unverified
10BCN+ELMoAccuracy54.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88—Unverified
2SVDCNNError4.74—Unverified
3LEAMError4.69—Unverified
4fastText, h=10, bigramError4.3—Unverified
5SWEM-hierError4.19—Unverified
6SRNNError3.96—Unverified
7M-ACNNError3.89—Unverified
8DNC+CUWError3.6—Unverified
9CCCapsNetError3.52—Unverified
10Block-sparse LSTMError3.27—Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500—Unverified
2VLAWEAccuracy93.3—Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5—Unverified
4AnglE-LLaMA-7BAccuracy91.09—Unverified
5byte mLSTM7Accuracy86.8—Unverified
6MEANAccuracy84.5—Unverified
7RNN-CapsuleAccuracy83.8—Unverified
8Capsule-BAccuracy82.3—Unverified
9SuBiLSTM-TiedAccuracy81.6—Unverified
10USE_T+CNNAccuracy81.59—Unverified