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 351–400 of 5630 papers

TitleStatusHype
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models—0
Subword Embedding from Bytes Gains Privacy without Sacrificing Accuracy and Complexity—0
Systematic Review: Text Processing Algorithms in Machine Learning and Deep Learning for Mental Health Detection on Social Media—0
AMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News DetectionCode1
Less is More: Parameter-Efficient Selection of Intermediate Tasks for Transfer LearningCode0
Enhancing Multimodal Sentiment Analysis for Missing Modality through Self-Distillation and Unified Modality Cross-AttentionCode1
Transit Pulse: Utilizing Social Media as a Source for Customer Feedback and Information Extraction with Large Language Model—0
Sentiment Analysis Based on RoBERTa for Amazon Review: An Empirical Study on Decision Making—0
You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools—0
Enhancing Cryptocurrency Market Forecasting: Advanced Machine Learning Techniques and Industrial Engineering Contributions—0
Utilizing Large Language Models for Event Deconstruction to Enhance Multimodal Aspect-Based Sentiment Analysis—0
Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies—0
Towards Hybrid Intelligence in Journalism: Findings and Lessons Learnt from a Collaborative Analysis of Greek Political Rhetoric by ChatGPT and Humans—0
Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability—0
PromptExp: Multi-granularity Prompt Explanation of Large Language Models—0
ERAS: Evaluating the Robustness of Chinese NLP Models to Morphological Garden Path Errors—0
Sarcasm Detection in a Less-Resourced LanguageCode0
Unifying Economic and Language Models for Enhanced Sentiment Analysis of the Oil Market—0
Experiences from Creating a Benchmark for Sentiment Classification for Varieties of English—0
Enhancing Assamese NLP Capabilities: Introducing a Centralized Dataset RepositoryCode0
In-Context Learning for Long-Context Sentiment Analysis on Infrastructure Project Opinions—0
Reducing Labeling Costs in Sentiment Analysis via Semi-Supervised Learning—0
News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep Learning Models—0
Modeling News Interactions and Influence for Financial Market Prediction—0
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets—0
Single Ground Truth Is Not Enough: Add Linguistic Variability to Aspect-based Sentiment Analysis Evaluation—0
State of NLP in Kenya: A Survey—0
Enhancing Affinity Propagation for Improved Public Sentiment InsightsCode0
Text Classification using Graph Convolutional Networks: A Comprehensive Survey—0
A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis—0
Inference and Verbalization Functions During In-Context LearningCode0
Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications—0
ALVIN: Active Learning Via INterpolation—0
Optimizing Transformer based on high-performance optimizer for predicting employment sentiment in American social media content—0
Stanceformer: Target-Aware Transformer for Stance DetectionCode0
DAdEE: Unsupervised Domain Adaptation in Early Exit PLMsCode0
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment AnalysisCode2
Language Model-Driven Data Pruning Enables Efficient Active Learning—0
Leveraging Fundamental Analysis for Stock Trend Prediction for Profit—0
Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis—0
Comparing zero-shot self-explanations with human rationales in multilingual text classification—0
Make Compound Sentences Simple to Analyze: Learning to Split Sentences for Aspect-based Sentiment AnalysisCode0
GCM-Net: Graph-enhanced Cross-Modal Infusion with a Metaheuristic-Driven Network for Video Sentiment and Emotion Analysis—0
Financial Sentiment Analysis on News and Reports Using Large Language Models and FinBERT—0
Linear Projections of Teacher Embeddings for Few-Class Distillation—0
Evaluating and explaining training strategies for zero-shot cross-lingual news sentiment analysis—0
Towards Robust Multimodal Sentiment Analysis with Incomplete DataCode2
Evaluation of OpenAI o1: Opportunities and Challenges of AGI—0
Multi-Source Hard and Soft Information Fusion Approach for Accurate Cryptocurrency Price Movement Prediction—0
GrEmLIn: A Repository of Green Baseline Embeddings for 87 Low-Resource Languages Injected with Multilingual Graph KnowledgeCode1
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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