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 3001–3050 of 5630 papers

TitleStatusHype
Evidence of disorientation towards immunization on online social media after contrasting political communication on vaccines. Results from an analysis of Twitter data in Italy—0
Revisiting Paraphrase Question Generator using Pairwise DiscriminatorCode0
Dirichlet uncertainty wrappers for actionable algorithm accuracy accountability and auditability—0
Language Independent Sentiment Analysis—0
Text Classification for Azerbaijani Language Using Machine Learning and Embedding—0
Simultaneous Identification of Tweet Purpose and Position—0
Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network—0
A Heterogeneous Graphical Model to Understand User-Level Sentiments in Social Media—0
Artificial mental phenomena: Psychophysics as a framework to detect perception biases in AI models—0
SemEval-2013 Task 2: Sentiment Analysis in Twitter—0
Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds—0
Unsupervised Transfer Learning via BERT Neuron Selection—0
Multilingual aspect clustering for sentiment analysisCode0
A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language ProcessingCode0
SemEval-2014 Task 9: Sentiment Analysis in Twitter—0
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution NetworksCode0
SemEval-2015 Task 10: Sentiment Analysis in Twitter—0
SemEval-2016 Task 4: Sentiment Analysis in TwitterCode0
SemEval-2017 Task 4: Sentiment Analysis in Twitter—0
Robust Deep Learning Based Sentiment Classification of Code-Mixed Text—0
Compositional De-Attention Networks—0
Sentiment Analysis of German Twitter—0
A Fine-Grained Sentiment Dataset for NorwegianCode0
Sentiment Analysis On Indian Indigenous Languages: A Review On Multilingual Opinion Mining—0
Language-Independent Sentiment Analysis Using Subjectivity and Positional Information—0
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis—0
Word-Class Embeddings for Multiclass Text ClassificationCode0
Low Rank Factorization for Compact Multi-Head Self-AttentionCode0
hauWE: Hausa Words Embedding for Natural Language Processing—0
Causally Denoise Word Embeddings Using Half-Sibling RegressionCode0
A Transformer-based approach to Irony and Sarcasm detectionCode0
Speech Sentiment Analysis via Pre-trained Features from End-to-end ASR Models—0
Paraphrasing with Large Language Models—0
Emotion Recognition for Vietnamese Social Media Text—0
Log Message Anomaly Detection and Classification Using Auto-B/LSTM and Auto-GRU—0
Multi-Zone Unit for Recurrent Neural Networks—0
Deep Learning versus Traditional Classifiers on Vietnamese Students' Feedback Corpus—0
Explanatory Masks for Neural Network Interpretability—0
Contextual Recurrent Units for Cloze-style Reading Comprehension—0
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis—0
LexiPers: An ontology based sentiment lexicon for PersianCode0
Analysing Russian Trolls via NLP tools—0
BP-Transformer: Modelling Long-Range Context via Binary PartitioningCode0
Generalizing Natural Language Analysis through Span-relation RepresentationsCode0
Learning to Few-Shot Learn Across Diverse Natural Language Classification TasksCode0
Subjective Sentiment Analysis for Arabic Newswire Comments—0
What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning—0
Making the Best Use of Review Summary for Sentiment AnalysisCode0
Word Embedding Algorithms as Generalized Low Rank Models and their Canonical Form—0
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic KnowledgeCode0
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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