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 3451–3500 of 5630 papers

TitleStatusHype
Training Complex Models with Multi-Task Weak SupervisionCode0
Inhibited Softmax for Uncertainty Estimation in Neural NetworksCode0
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep LearningCode0
Sentiment analysis under temporal shift—0
Word Embeddings for Code-Mixed Language Processing—0
Siamese Network-Based Supervised Topic Modeling—0
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification—0
KU-CST at CoNLL--SIGMORPHON 2018 Shared Task: a Tridirectional Model—0
Memory, Show the Way: Memory Based Few Shot Word Representation Learning—0
Speed Reading: Learning to Read ForBackward via ShuttleCode0
Saying no but meaning yes: negation and sentiment analysis in Basque—0
Sequence Classification with Human AttentionCode0
Joint Learning for Targeted Sentiment Analysis—0
Limbic: Author-Based Sentiment Aspect Modeling Regularized with Word Embeddings and Discourse Relations—0
An LSTM Approach to Short Text Sentiment Classification with Word Embeddings—0
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images—0
Arabizi sentiment analysis based on transliteration and automatic corpus annotation—0
Linking News Sentiment to Microblogs: A Distributional Semantics Approach to Enhance Microblog Sentiment Classification—0
UWB at IEST 2018: Emotion Prediction in Tweets with Bidirectional Long Short-Term Memory Neural Network—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
Personalized Microblog Sentiment Classification via Adversarial Cross-lingual Multi-task Learning—0
Heuristically Informed Unsupervised Idiom Usage Recognition—0
Multi-grained Attention Network for Aspect-Level Sentiment Classification—0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs—0
Aspect Based Sentiment Analysis into the Wild—0
Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment AnalysisCode0
Language Independent Sentiment Analysis with Sentiment-Specific Word Embeddings—0
In-domain Context-aware Token Embeddings Improve Biomedical Named Entity Recognition—0
結合卷積神經網路與遞迴神經網路於推文極性分類 (Combining Convolutional Neural Network and Recurrent Neural Network for Tweet Polarity Classification) [In Chinese]—0
Syntactical Analysis of the Weaknesses of Sentiment Analyzers—0
Contextual Inter-modal Attention for Multi-modal Sentiment AnalysisCode0
Sentiment Expression Boundaries in Sentiment Polarity Classification—0
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network—0
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion ClassificationCode0
Sentiment Classification towards Question-Answering with Hierarchical Matching Network—0
Attentive Gated Lexicon Reader with Contrastive Contextual Co-Attention for Sentiment Classification—0
Thumbs Up and Down: Sentiment Analysis of Medical Online Forums—0
Resources to Examine the Quality of Word Embedding Models Trained on n-Gram Data—0
The Role of Emotions in Native Language Identification—0
Creating a Dataset for Multilingual Fine-grained Emotion-detection Using Gamification-based Annotation—0
Efficient and Accurate Abnormality Mining from Radiology Reports with Customized False Positive Reduction—0
NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification—0
Learning Scalar Adjective Intensity from Paraphrases—0
NL-FIIT at IEST-2018: Emotion Recognition utilizing Neural Networks and Multi-level PreprocessingCode0
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection—0
Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions—0
IARM: Inter-Aspect Relation Modeling with Memory Networks in Aspect-Based Sentiment AnalysisCode0
Learning representations for sentiment classification using Multi-task framework—0
Hybrid Neural Attention for Agreement/Disagreement Inference in Online Debates—0
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNsCode0
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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