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 32013250 of 5630 papers

TitleStatusHype
Unsupervised machine learning to analyse city logistics through Twitter0
An Interactive Multi-Task Learning Network for End-to-End Aspect-Based Sentiment AnalysisCode1
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis0
On the Effect of Word Order on Cross-lingual Sentiment Analysis0
Sentiment analysis is not solved! Assessing and probing sentiment classificationCode0
Using Structured Representation and Data: A Hybrid Model for Negation and Sentiment in Customer Service Conversations0
Modeling Sentiment Dependencies with Graph Convolutional Networks for Aspect-level Sentiment ClassificationCode0
Open-Domain Targeted Sentiment Analysis via Span-Based Extraction and ClassificationCode0
Forward and Backward Knowledge Transfer for Sentiment Classification0
Detection and Prediction of Users Attitude Based on Real-Time and Batch Sentiment Analysis of Facebook Comments0
Gradual Machine Learning for Aspect-level Sentiment Analysis0
Shift-of-Perspective Identification Within Legal Cases0
OutdoorSent: Sentiment Analysis of Urban Outdoor Images by Using Semantic and Deep Features0
DOER: Dual Cross-Shared RNN for Aspect Term-Polarity Co-ExtractionCode0
Towards Lossless Encoding of SentencesCode0
Progressive Self-Supervised Attention Learning for Aspect-Level Sentiment AnalysisCode1
System Demo for Transfer Learning across Vision and Text using Domain Specific CNN Accelerator for On-Device NLP Applications0
A Study of Feature Extraction techniques for Sentiment Analysis0
Sentiment Tagging with Partial Labels using Modular ArchitecturesCode0
Multi-task Pairwise Neural Ranking for Hashtag SegmentationCode0
Cross-lingual Subjectivity Detection for Resource Lean Languages0
A Soft Label Strategy for Target-Level Sentiment Classification0
SPARSE: Structured Prediction using Argument-Relative Structured Encoding0
Towards discourse annotation and sentiment analysis of the Basque Opinion Corpus0
Annotating with Pros and Cons of Technologies in Computer Science PapersCode0
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification0
Team Kermit-the-frog at SemEval-2019 Task 4: Bias Detection Through Sentiment Analysis and Simple Linguistic Features0
MILAB at SemEval-2019 Task 3: Multi-View Turn-by-Turn Model for Context-Aware Sentiment Analysis0
Learning Bilingual Sentiment-Specific Word Embeddings without Cross-lingual Supervision0
SCIA at SemEval-2019 Task 3: Sentiment Analysis in Textual Conversations Using Deep Learning0
Modeling Personal Biases in Language Use by Inducing Personalized Word Embeddings0
Adversarial Category Alignment Network for Cross-domain Sentiment Classification0
Harvey Mudd College at SemEval-2019 Task 4: The D.X. Beaumont Hyperpartisan News Detector0
Learning Interpretable Negation Rules via Weak Supervision at Document Level: A Reinforcement Learning Approach0
Target-oriented Opinion Words Extraction with Target-fused Neural Sequence LabelingCode0
Hybrid RNN at SemEval-2019 Task 9: Blending Information Sources for Domain-Independent Suggestion Mining0
YUN-HPCC at SemEval-2019 Task 3: Multi-Step Ensemble Neural Network for Sentiment Analysis in Textual Conversation0
Incorporating Emoji Descriptions Improves Tweet Classification0
NTUA-ISLab at SemEval-2019 Task 3: Determining emotions in contextual conversations with deep learning0
UAIC at SemEval-2019 Task 3: Extracting Much from Little0
Harvey Mudd College at SemEval-2019 Task 4: The Carl Kolchak Hyperpartisan News Detector0
Examining Structure of Word Embeddings with PCA0
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English0
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble LearningCode0
Investigating an Effective Character-level Embedding in Korean Sentence ClassificationCode0
LANGUAGE MODEL EMBEDDINGS IMPROVE SENTIMENT ANALYSIS IN RUSSIANCode0
Differential Privacy Has Disparate Impact on Model AccuracyCode0
Leap-LSTM: Enhancing Long Short-Term Memory for Text CategorizationCode0
EDUCE: Explaining model Decisions through Unsupervised Concepts Extraction0
Combating Adversarial Misspellings with Robust Word RecognitionCode0
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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