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 3051–3100 of 5630 papers

TitleStatusHype
Coverage Guided Testing for Recurrent Neural Networks—0
Learning from Explanations with Neural Execution TreeCode0
A Failure of Aspect Sentiment Classifiers and an Adaptive Re-weighting SolutionCode0
Sentiment analysis model for Twitter data in Polish language—0
An Algorithm for Routing Capsules in All Domains—0
Slang Detection and Identification—0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge—0
A Challenge Dataset and Effective Models for Aspect-Based Sentiment AnalysisCode0
Recognizing Conflict Opinions in Aspect-level Sentiment Classification with Dual Attention Networks—0
Multi-Task Stance Detection with Sentiment and Stance Lexicons—0
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews—0
Multilingual Model Using Cross-Task Embedding Projection—0
A Knowledge Regularized Hierarchical Approach for Emotion Cause Analysis—0
Natural Language Generation for Effective Knowledge DistillationCode0
A Robust Self-Learning Framework for Cross-Lingual Text Classification—0
Multi-Level Sentiment Analysis of PolEmo 2.0: Extended Corpus of Multi-Domain Consumer Reviews—0
A Comparative Analysis of Unsupervised Language Adaptation Methods—0
Investigating Dynamic Routing in Tree-Structured LSTM for Sentiment Analysis—0
A deep-learning framework to detect sarcasm targets—0
Fine-tune BERT with Sparse Self-Attention Mechanism—0
Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree—0
To Annotate or Not? Predicting Performance Drop under Domain Shift—0
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data—0
Towards a Unified End-to-End Approach for Fully Unsupervised Cross-Lingual Sentiment Analysis—0
Efficient Feature Selection techniques for Sentiment Analysis—0
LexicalAT: Lexical-Based Adversarial Reinforcement Training for Robust Sentiment Classification—0
Capsule Network with Interactive Attention for Aspect-Level Sentiment Classification—0
Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness—0
Confident Learning: Estimating Uncertainty in Dataset LabelsCode0
Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial LearningCode0
Predicting Discourse Structure using Distant Supervision from Sentiment—0
An Efficient Model for Sentiment Analysis of Electronic Product Reviews in Vietnamese—0
Understand customer reviews with less data and in short time: pretrained language representation and active learning—0
Weakly-Supervised Deep Learning for Domain Invariant Sentiment Classification—0
A Game Theoretic Approach to Class-wise Selective RationalizationCode0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
Sentiment Analysis for Arabic in Social Media Network: A Systematic Mapping Study—0
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer EncodersCode0
Selective Attention Based Graph Convolutional Networks for Aspect-Level Sentiment Classification—0
Human-Like Decision Making: Document-level Aspect Sentiment Classification via Hierarchical Reinforcement Learning—0
Improving Sequence Modeling Ability of Recurrent Neural Networks via SememesCode0
An Intelligent Data Analysis for Hotel Recommendation Systems using Machine Learning—0
Going Negative Online? -- A Study of Negative Advertising on Social Media—0
SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media TextCode0
Sentiment Analysis from Images of Natural Disasters—0
Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction—0
Perturbation Sensitivity Analysis to Detect Unintended Model Biases—0
SentiCite: An Approach for Publication Sentiment Analysis—0
Fine-grained Sentiment Classification using BERTCode0
The merits of Universal Language Model Fine-tuning for Small Datasets -- a case with Dutch book reviewsCode0
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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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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