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 601–650 of 5630 papers

TitleStatusHype
E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple PredictionCode0
Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks—0
SaudiBERT: A Large Language Model Pretrained on Saudi Dialect CorporaCode0
Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness—0
Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis—0
Analyzing Language Bias Between French and English in Conventional Multilingual Sentiment Analysis ModelsCode0
Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development—0
Fine-grained Speech Sentiment Analysis in Chinese Psychological Support Hotlines Based on Large-scale Pre-trained ModelCode0
Can citations tell us about a paper's reproducibility? A case study of machine learning papersCode0
QuakeBERT: Accurate Classification of Social Media Texts for Rapid Earthquake Impact Assessment—0
Sentiment Analysis Across Languages: Evaluation Before and After Machine Translation to EnglishCode0
The Call for Socially Aware Language Technologies—0
What is Sentiment Meant to Mean to Language Models?—0
Joint sentiment analysis of lyrics and audio in music—0
UniGen: Universal Domain Generalization for Sentiment Classification via Zero-shot Dataset GenerationCode0
Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private StatesCode0
New Benchmark Dataset and Fine-Grained Cross-Modal Fusion Framework for Vietnamese Multimodal Aspect-Category Sentiment AnalysisCode1
Aspect and Opinion Term Extraction Using Graph Attention Network—0
CLASSP: a Biologically-Inspired Approach to Continual Learning through Adjustment Suppression and Sparsity PromotionCode0
Human-in-the-Loop Synthetic Text Data Inspection with Provenance TrackingCode0
It's Difficult to be Neutral -- Human and LLM-based Sentiment Annotation of Patient Comments—0
Transfer Learning and Transformer Architecture for Financial Sentiment Analysis—0
Modeling Orthographic Variation Improves NLP Performance for Nigerian Pidgin—0
LLMs for Generating and Evaluating Counterfactuals: A Comprehensive StudyCode0
Trust Dynamics and Market Behavior in Cryptocurrency: A Comparative Study of Centralized and Decentralized ExchangesCode0
Text Sentiment Analysis and Classification Based on Bidirectional Gated Recurrent Units (GRUs) Model—0
Investigating the dissemination of STEM content on social media with computational tools—0
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
Does It Make Sense to Explain a Black Box With Another Black Box?Code0
Embarrassingly Simple Unsupervised Aspect Based Sentiment Tuple Extraction—0
Sample Design Engineering: An Empirical Study of What Makes Good Downstream Fine-Tuning Samples for LLMsCode5
Cooperative Sentiment Agents for Multimodal Sentiment AnalysisCode1
Large Language Models in Targeted Sentiment AnalysisCode1
Comparative Analysis of Deep Natural Networks and Large Language Models for Aspect-Based Sentiment AnalysisCode0
Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis—0
Do LLMs Think Fast and Slow? A Causal Study on Sentiment AnalysisCode0
Relational Graph Convolutional Networks for Sentiment Analysis—0
A Sentiment Analysis of Medical Text Based on Deep Learning—0
Trustworthy Multimodal Fusion for Sentiment Analysis in Ordinal Sentiment Space—0
ArSen-20: A New Benchmark for Arabic Sentiment DetectionCode0
Accuracy of a Large Language Model in Distinguishing Anti- And Pro-vaccination Messages on Social Media: The Case of Human Papillomavirus Vaccination—0
Finding fake reviews in e-commerce platforms by using hybrid algorithms—0
Heuristic-enhanced Candidates Selection strategy for GPTs tackle Few-Shot Aspect-Based Sentiment AnalysisCode0
EFSA: Towards Event-Level Financial Sentiment AnalysisCode0
EcoVerse: An Annotated Twitter Dataset for Eco-Relevance Classification, Environmental Impact Analysis, and Stance DetectionCode0
Semantic Stealth: Adversarial Text Attacks on NLP Using Several Methods—0
Plug and Play with Prompts: A Prompt Tuning Approach for Controlling Text Generation—0
TCAN: Text-oriented Cross Attention Network for Multimodal Sentiment Analysis—0
Sentiment analysis and random forest to classify LLM versus human source applied to Scientific Texts—0
Deciphering Political Entity Sentiment in News with Large Language Models: Zero-Shot and Few-Shot StrategiesCode0
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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