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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3101–3150 of 5630 papers

TitleStatusHype
Improved Word Sense Disambiguation Using Pre-Trained Contextualized Word RepresentationsCode0
Machine Translation for Machines: the Sentiment Classification Use Case—0
A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks—0
Weakly Supervised Attention Networks for Fine-Grained Opinion Mining and Public Health—0
Stock Market Forecasting Based on Text Mining Technology: A Support Vector Machine Method—0
Learning the Difference that Makes a Difference with Counterfactually-Augmented DataCode0
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
Amharic Negation Handling—0
Neural Linear Bandits: Overcoming Catastrophic Forgetting through Likelihood MatchingCode0
AdvCodec: Towards A Unified Framework for Adversarial Text Generation—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
Multi-Dimensional Explanation of Reviews—0
Multi-Dimensional Explanation of Target Variables from Documents—0
Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis—0
A novel Bayesian estimation-based word embedding model for sentiment analysis—0
Teacher-Student Learning Paradigm for Tri-training: An Efficient Method for Unlabeled Data Exploitation—0
The Power of Communities: A Text Classification Model with Automated Labeling Process Using Network Community Detection—0
EDUCE: Explaining model Decision through Unsupervised Concepts Extraction—0
Syntax-Aware Aspect-Level Sentiment Classification with Proximity-Weighted Convolution NetworkCode0
TinyBERT: Distilling BERT for Natural Language UnderstandingCode0
Dual Adversarial Co-Learning for Multi-Domain Text Classification—0
Text Length Adaptation in Sentiment ClassificationCode0
Weighed Domain-Invariant Representation Learning for Cross-domain Sentiment Analysis—0
Sentiment-Aware Recommendation System for Healthcare using Social Media—0
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model—0
Learning Explicit and Implicit Structures for Targeted Sentiment Analysis—0
Transfer Learning with Dynamic Distribution Adaptation—0
Delivering Cognitive Behavioral Therapy Using A Conversational SocialRobot—0
Parameterized Convolutional Neural Networks for Aspect Level Sentiment Classification—0
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT—0
Comprehensive Analysis of Aspect Term Extraction Methods using Various Text Embeddings—0
Domain Aggregation Networks for Multi-Source Domain Adaptation—0
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation—0
Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional NetworksCode0
RNN Architecture Learning with Sparse RegularizationCode0
Natural Adversarial Sentence Generation with Gradient-based PerturbationCode0
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text ClassificationCode0
Syntax-Aware Aspect Level Sentiment Classification with Graph Attention Networks—0
Extracting Aspects Hierarchies using Rhetorical Structure Theory—0
LIT: Learned Intermediate Representation Training for Model CompressionCode0
Certified Robustness to Adversarial Word SubstitutionsCode0
A Study on Game Review Summarization—0
Annotating evaluative sentences for sentiment analysis: a dataset for NorwegianCode0
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment AnalysisCode0
Aspect-Based Sentiment Analysis using BERT—0
Cross-Domain Sentiment Classification using Vector Embedded Domain Representations—0
From Image to Text in Sentiment Analysis via Regression and Deep Learning—0
Lexicon information in neural sentiment analysis: a multi-task learning approachCode0
Monitoring stance towards vaccination in Twitter messages—0
Predicting Sentiment of Polish Language Short Texts—0
Show:102550
← PrevPage 63 of 113Next →
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