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 2301–2350 of 5630 papers

TitleStatusHype
Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)—0
A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis—0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification—0
Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for Croatian—0
A Term Extraction Approach to Survey Analysis in Health Care—0
DeBERTinha: A Multistep Approach to Adapt DebertaV3 XSmall for Brazilian Portuguese Natural Language Processing Task—0
A Temporal Psycholinguistics Approach to Identity Resolution of Social Media Users—0
DCU: Aspect-based Polarity Classification for SemEval Task 4—0
Atalaya at TASS 2019: Data Augmentation and Robust Embeddings for Sentiment Analysis—0
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification—0
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification—0
Analyzing Modality Robustness in Multimodal Sentiment Analysis—0
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions—0
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks—0
A Broad-Coverage Normalization System for Social Media Language—0
Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment Analysis—0
A System to Filter out Unwanted Social Media Content in Real-time on iPhones—0
Data Sets: Word Embeddings Learned from Tweets and General Data—0
Datasets for Aspect-Based Sentiment Analysis in French—0
A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPAs LORELEI project—0
Dataset of Philippine Presidents Speeches from 1935 to 2016—0
A System for Real-time Twitter Sentiment Analysis of 2012 U.S. Presidential Election Cycle—0
Data Set for Stance and Sentiment Analysis from User Comments on Croatian News—0
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language—0
A system for fine-grained aspect-based sentiment analysis of Chinese—0
Analyzing Gender Bias in Student Evaluations—0
Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies—0
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision—0
Dataset and Baseline for Automatic Student Feedback Analysis—0
A System for Extracting Sentiment from Large-Scale Arabic Social Data—0
A systematic review of early warning systems in finance—0
Analyzing Features for the Detection of Happy Endings in German Novels—0
Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
A Systematic Review of Aspect-based Sentiment Analysis: Domains, Methods, and Trends—0
Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification—0
A Systematic Analysis on the Temporal Generalization of Language Models in Social Media—0
Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME—0
Advancing Humor-Focused Sentiment Analysis through Improved Contextualized Embeddings and Model Architecture—0
A Comparison of Techniques for Sentiment Classification of Film Reviews—0
Data augmentation for low resource sentiment analysis using generative adversarial networks—0
DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning—0
Analyzing ELMo and DistilBERT on Socio-political News Classification—0
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation—0
DAG-Structured Long Short-Term Memory for Semantic Compositionality—0
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks—0
DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter—0
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques—0
Analyzing Curriculum Learning for Sentiment Analysis along Task Difficulty, Pacing and Visualization Axes—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