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 34513500 of 5630 papers

TitleStatusHype
Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment AnalysisCode0
KU-CST at CoNLL--SIGMORPHON 2018 Shared Task: a Tridirectional Model0
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates0
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment AnalysisCode0
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning0
Learning Scalar Adjective Intensity from Paraphrases0
Memory, Show the Way: Memory Based Few Shot Word Representation Learning0
Multi-grained Attention Network for Aspect-Level Sentiment Classification0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations0
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network0
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNsCode0
Siamese Network-Based Supervised Topic Modeling0
Speed Reading: Learning to Read ForBackward via ShuttleCode0
Word Embeddings for Code-Mixed Language Processing0
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification0
Heuristically Informed Unsupervised Idiom Usage Recognition0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs0
Sentiment Classification towards Question-Answering with Hierarchical Matching Network0
Syntactical Analysis of the Weaknesses of Sentiment Analyzers0
Joint Learning for Targeted Sentiment Analysis0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images0
Contextual Inter-modal Attention for Multi-modal Sentiment AnalysisCode0
Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction0
Real Time Monitoring of Social Media and Digital Press0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING0
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives0
Argumentation Mining: Exploiting Multiple Sources and Background Knowledge0
Dual Memory Network Model for Biased Product Review Classification0
Graph Convolutional Networks for Text ClassificationCode1
Emo2Vec: Learning Generalized Emotion Representation by Multi-task TrainingCode0
Sentiment analysis for Arabic language: A brief survey of approaches and techniques0
Dynamic Compositionality in Recursive Neural Networks with Structure-aware Tag RepresentationsCode0
Multi-Source Domain Adaptation with Mixture of ExpertsCode0
Cell-aware Stacked LSTMs for Modeling Sentences0
Deep learning for language understanding of mental health concepts derived from Cognitive Behavioural TherapyCode0
Adaptive Semi-supervised Learning for Cross-domain Sentiment ClassificationCode0
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion ClassificationCode0
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs0
Iterative Recursive Attention Model for Interpretable Sequence Classification0
Comparative Studies of Detecting Abusive Language on TwitterCode1
Identifying the sentiment styles of YouTube's vloggers0
On Tree-Based Neural Sentence ModelingCode0
Distance Based Source Domain Selection for Sentiment Classification0
Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks0
Convolutional Neural Networks with Recurrent Neural FiltersCode0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set0
From Random to Supervised: A Novel Dropout Mechanism Integrated with Global InformationCode0
Revisiting the Importance of Encoding Logic Rules in Sentiment ClassificationCode0
Financial Aspect-Based Sentiment Analysis using Deep Representations0
Emoji Sentiment Scores of Writers using Odds Ratio and Fisher Exact Test0
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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