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 1–50 of 5630 papers

TitleStatusHype
AdaptiSent: Context-Aware Adaptive Attention for Multimodal Aspect-Based Sentiment Analysis—0
DCR: Quantifying Data Contamination in LLMs EvaluationCode0
AI Wizards at CheckThat! 2025: Enhancing Transformer-Based Embeddings with Sentiment for Subjectivity Detection in News ArticlesCode0
SentiDrop: A Multi Modal Machine Learning model for Predicting Dropout in Distance Learning—0
GNN-CNN: An Efficient Hybrid Model of Convolutional and Graph Neural Networks for Text RepresentationCode0
FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs—0
Unpacking Generative AI in Education: Computational Modeling of Teacher and Student Perspectives in Social Media Discourse—0
Characterizing Linguistic Shifts in Croatian News via Diachronic Word EmbeddingsCode0
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing—0
Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets—0
Analyzing Emotions in Bangla Social Media Comments Using Machine Learning and LIME—0
AraReasoner: Evaluating Reasoning-Based LLMs for Arabic NLP—0
Quantum Graph Transformer for NLP Sentiment Classification—0
Artificial Intelligence and Civil Discourse: How LLMs Moderate Climate Change Conversations—0
CAtCh: Cognitive Assessment through Cookie ThiefCode0
How do datasets, developers, and models affect biases in a low-resourced language?—0
Sentiment Analysis in Learning Management Systems Understanding Student Feedback at Scale—0
Reasoning or Overthinking: Evaluating Large Language Models on Financial Sentiment Analysis—0
Invariance Makes LLM Unlearning Resilient Even to Unanticipated Downstream Fine-TuningCode0
FinBERT2: A Specialized Bidirectional Encoder for Bridging the Gap in Finance-Specific Deployment of Large Language Models—0
MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMsCode0
Multi-Domain ABSA Conversation Dataset Generation via LLMs for Real-World Evaluation and Model Comparison—0
Predicting Human Depression with Hybrid Data Acquisition utilizing Physical Activity Sensing and Social Media Feeds—0
Sentiment Simulation using Generative AI Agents—0
Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification—0
The Role of Diversity in In-Context Learning for Large Language Models—0
CrosGrpsABS: Cross-Attention over Syntactic and Semantic Graphs for Aspect-Based Sentiment Analysis in a Low-Resource Language—0
Hermes@DravidianLangTech 2025: Sentiment Analysis of Dravidian Languages using XLM-RoBERTaCode0
Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla—0
Task Specific Pruning with LLM-Sieve: How Many Parameters Does Your Task Really Need?—0
LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing—0
Omni TM-AE: A Scalable and Interpretable Embedding Model Using the Full Tsetlin Machine State Space—0
An Empirical Study on Configuring In-Context Learning Demonstrations for Unleashing MLLMs' Sentimental Perception Capability—0
Can AI Read Between The Lines? Benchmarking LLMs On Financial Nuance—0
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
DeFTX: Denoised Sparse Fine-Tuning for Zero-Shot Cross-Lingual Transfer—0
Nek Minit: Harnessing Pragmatic Metacognitive Prompting for Explainable Sarcasm Detection of Australian and Indian English—0
Reliable Decision Support with LLMs: A Framework for Evaluating Consistency in Binary Text Classification Applications—0
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition—0
A Personalized Conversational Benchmark: Towards Simulating Personalized ConversationsCode1
PL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore—0
Enhanced Multimodal Aspect-Based Sentiment Analysis by LLM-Generated Rationales—0
Picturized and Recited with Dialects: A Multimodal Chinese Representation Framework for Sentiment Analysis of Classical Chinese PoetryCode0
LAMP: Extracting Locally Linear Decision Surfaces from LLM World Models—0
Conversational Recommendation System using NLP and Sentiment Analysis—0
Emotion Recognition for Low-Resource Turkish: Fine-Tuning BERTurk on TREMO and Testing on Xenophobic Political Discourse—0
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency—0
Token Masking Improves Transformer-Based Text Classification—0
Low-Resource Language Processing: An OCR-Driven Summarization and Translation PipelineCode0
Private Transformer Inference in MLaaS: A Survey—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
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