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 3901–3950 of 5630 papers

TitleStatusHype
GPU Kernels for Block-Sparse WeightsCode0
Leveraging Linguistic Resources for Improving Neural Text Classification—0
NCTU-NTUT at IJCNLP-2017 Task 2: Deep Phrase Embedding using bi-LSTMs for Valence-Arousal Ratings Prediction of Chinese Phrases—0
CIAL at IJCNLP-2017 Task 2: An Ensemble Valence-Arousal Analysis System for Chinese Words and Phrases—0
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network—0
THU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM—0
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases—0
MainiwayAI at IJCNLP-2017 Task 2: Ensembles of Deep Architectures for Valence-Arousal Prediction—0
ADAPT at IJCNLP-2017 Task 4: A Multinomial Naive Bayes Classification Approach for Customer Feedback Analysis task—0
NCYU at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases using Vector Representations—0
LDCCNLP at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases Using Machine Learning—0
NLPSA at IJCNLP-2017 Task 2: Imagine Scenario: Leveraging Supportive Images for Dimensional Sentiment Analysis—0
YNU-HPCC at IJCNLP-2017 Task 4: Attention-based Bi-directional GRU Model for Customer Feedback Analysis Task of English—0
CKIP at IJCNLP-2017 Task 2: Neural Valence-Arousal Prediction for Phrases—0
SentiNLP at IJCNLP-2017 Task 4: Customer Feedback Analysis Using a Bi-LSTM-CNN Model—0
Bingo at IJCNLP-2017 Task 4: Augmenting Data using Machine Translation for Cross-linguistic Customer Feedback Classification—0
NTOUA at IJCNLP-2017 Task 2: Predicting Sentiment Scores of Chinese Words and Phrases—0
IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases—0
The Sentimental Value of Chinese Sub-Character Components—0
Multiple Instance Learning Networks for Fine-Grained Sentiment AnalysisCode0
Improving the Accuracy of Pre-trained Word Embeddings for Sentiment AnalysisCode0
Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks—0
10Sent: A Stable Sentiment Analysis Method Based on the Combination of Off-The-Shelf Approaches—0
Non-Contextual Modeling of Sarcasm using a Neural Network Benchmark—0
Robust Unsupervised Domain Adaptation for Neural Networks via Moment AlignmentCode0
A Sequential Neural Encoder with Latent Structured Description for Modeling Sentences—0
Sentiment analysis of twitter data—0
Multiple-Source Adaptation for Regression Problems—0
Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machine—0
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model—0
Bayesian Paragraph Vectors—0
Quality-Efficiency Trade-offs in Machine Learning for Text Processing—0
Aspect-level Sentiment Classification with HEAT (HiErarchical ATtention) Network—0
Fine-tuning Tree-LSTM for phrase-level sentiment classification on a Polish dependency treebank. Submission to PolEval task 2Code0
Compressing Word Embeddings via Deep Compositional Code LearningCode0
Toward Contextual Valence Shifters in Vietnamese Reviews—0
Multi-Channel Lexicon Integrated CNN-BiLSTM Models for Sentiment Analysis—0
Multi-Domain Aspect Extraction Using Support Vector Machines—0
Ensemble Technique Utilization for Indonesian Dependency Parser—0
Geographical Evaluation of Word Embeddings—0
Implicit Syntactic Features for Target-dependent Sentiment Analysis—0
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews—0
Towards Bootstrapping a Polarity Shifter Lexicon using Linguistic Features—0
Sentence Modeling with Deep Neural Architecture using Lexicon and Character Attention Mechanism for Sentiment Classification—0
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis—0
Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification—0
Estimating Reactions and Recommending Products with Generative Models of Reviews—0
An Ensemble Method with Sentiment Features and Clustering Support—0
Text Sentiment Analysis based on Fusion of Structural Information and Serialization Information—0
Graph Based Sentiment Aggregation using ConceptNet Ontology—0
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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