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 2201–2250 of 5630 papers

TitleStatusHype
Topic Modeling, Clade-assisted Sentiment Analysis, and Vaccine Brand Reputation Analysis of COVID-19 Vaccine-related Facebook Comments in the PhilippinesCode0
Calling to CNN-LSTM for Rumor Detection: A Deep Multi-channel Model for Message Veracity Classification in Microblogs—0
Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions—0
Unsupervised Multimodal Language Representations using Convolutional Autoencoders—0
Using Psuedolabels for training Sentiment Classifiers makes the model generalize better across datasets—0
Exploring Conditional Text Generation for Aspect-Based Sentiment AnalysisCode0
Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning—0
Relation Analysis between Hotel Review Rating Scores and Sentiment Analysis of Reviews by Chinese Tourists Visiting Japan—0
A Comparative Study of Sentiment Analysis Using NLP and Different Machine Learning Techniques on US Airline Twitter Data—0
What confuses BERT? Linguistic Evaluation of Sentiment Analysis on Telecom Customer Opinion—0
ROCLING-2021 Shared Task: Dimensional Sentiment Analysis for Educational Texts—0
Aggregating User-Centric and Post-Centric Sentiments from Social Media for Topical Stance Prediction—0
Home Appliance Review Research Via Adversarial Reptile—0
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification—0
A Corpus for Dimensional Sentiment Classification on YouTube Streaming Service—0
MMTL: The Meta Multi-Task Learning for Aspect Category Sentiment Analysis—0
UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis—0
ntust-nlp-2 at ROCLING-2021 Shared Task: BERT-based semantic analyzer with word-level information—0
CYUT at ROCLING-2021 Shared Task: Based on BERT and MacBERT—0
Using Valence and Arousal-infused Bi-LSTM for Sentiment Analysis in Social Media Product Reviews—0
SCUDS at ROCLING-2021 Shared Task: Using Pretrained Model for Dimensional Sentiment Analysis Based on Sample Expansion Method—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
ntust-nlp-1 at ROCLING-2021 Shared Task: Educational Texts Dimensional Sentiment Analysis using Pretrained Language Models—0
SoochowDS at ROCLING-2021 Shared Task: Text Sentiment Analysis Using BERT and LSTM—0
NCU-NLP at ROCLING-2021 Shared Task: Using MacBERT Transformers for Dimensional Sentiment Analysis—0
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
Adversarial Training: A simple and efficient technique to Improving NLP Robustness—0
Improving Sentiment Classification Using 0-Shot Generated Labels for Custom Transformer Embeddings—0
Overview of the Arabic Sentiment Analysis 2021 Competition at KAUST—0
Classifying Tweet Sentiment Using the Hidden State and Attention Matrix of a Fine-tuned BERTweet Model—0
Neural Dependency Coding inspired Multimodal Fusion—0
Evaluation of Non-Negative Matrix Factorization and n-stage Latent Dirichlet Allocation for Emotion Analysis in Turkish Tweets—0
Sentiment Analysis in Twitter for Macedonian—0
Electoral Programs of German Parties 2021: A Computational Analysis Of Their Comprehensibility and Likeability Based On SentiArt—0
Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)—0
A Comprehensive Review on Summarizing Financial News Using Deep Learning—0
Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language—0
A Comprehensive Overview of Recommender System and Sentiment Analysis—0
General Cross-Architecture Distillation of Pretrained Language Models into Matrix EmbeddingsCode0
A Multimodal Sentiment Dataset for Video Recommendation—0
SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis—0
Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian ReviewsCode0
Sentiment Analysis in Poems in Misurata Sub-dialect -- A Sentiment Detection in an Arabic Sub-dialect—0
Dialog speech sentiment classification for imbalanced datasets—0
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis—0
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding—0
Sparse Fuzzy Attention for Structured Sentiment Analysis—0
Stock Price Prediction Under Anomalous Circumstances—0
Oscillatory Fourier Neural Network: A Compact and Efficient Architecture for Sequential Processing—0
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text ClassificationCode0
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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
2T5-11BAccuracy97.5—Unverified
3MT-DNN-SMARTAccuracy97.5—Unverified
4T5-3BAccuracy97.4—Unverified
5MUPPET Roberta LargeAccuracy97.4—Unverified
6StructBERTRoBERTa ensembleAccuracy97.1—Unverified
7ALBERTAccuracy97.1—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