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 801–850 of 5630 papers

TitleStatusHype
PowMix: A Versatile Regularizer for Multimodal Sentiment Analysis—0
Geo-located Aspect Based Sentiment Analysis (ABSA) for Crowdsourced Evaluation of Urban Environments—0
Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets—0
An Extended Variational Mode Decomposition Algorithm Developed Speech Emotion Recognition PerformanceCode0
Aspect-Based Sentiment Analysis with Explicit Sentiment Augmentations—0
Sentiment Analysis and Text Analysis of the Public Discourse on Twitter about COVID-19 and MPox—0
A Soft Contrastive Learning-based Prompt Model for Few-shot Sentiment Analysis—0
Generative Context-aware Fine-tuning of Self-supervised Speech Models—0
Discovering Highly Influential Shortcut Reasoning: An Automated Template-Free ApproachCode0
A Comparative Analysis of Fine-Tuned LLMs and Few-Shot Learning of LLMs for Financial Sentiment Analysis—0
A Novel Energy based Model Mechanism for Multi-modal Aspect-Based Sentiment AnalysisCode1
Deep Learning-based Sentiment Classification: A Comparative Survey—0
Multimodal Sentiment Analysis: Perceived vs Induced Sentiments—0
Sentiment analysis in Tourism: Fine-tuning BERT or sentence embeddings concatenation?—0
User Friendly and Adaptable Discriminative AI: Using the Lessons from the Success of LLMs and Image Generation Models—0
Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification—0
Perceiving University Student's Opinions from Google App Reviews—0
A Review of Hybrid and Ensemble in Deep Learning for Natural Language Processing—0
GPT-4V with Emotion: A Zero-shot Benchmark for Generalized Emotion RecognitionCode1
From Big to Small Without Losing It All: Text Augmentation with ChatGPT for Efficient Sentiment AnalysisCode0
Deep Emotions Across Languages: A Novel Approach for Sentiment Propagation in Multilingual WordNetsCode0
Corporate Bankruptcy Prediction with Domain-Adapted BERT—0
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation—0
Knowledge Graph Enhanced Aspect-Level Sentiment Analysis—0
Understanding Opinions Towards Climate Change on Social Media—0
Exploring a Hybrid Deep Learning Framework to Automatically Discover Topic and Sentiment in COVID-19 Tweets—0
From Voices to Validity: Leveraging Large Language Models (LLMs) for Textual Analysis of Policy Stakeholder Interviews—0
PipeOptim: Ensuring Effective 1F1B Schedule with Optimizer-Dependent Weight PredictionCode0
Easy Data Augmentation in Sentiment Analysis of Cyberbullying—0
TurkishBERTweet: Fast and Reliable Large Language Model for Social Media AnalysisCode1
Syntax-Informed Interactive Model for Comprehensive Aspect-Based Sentiment Analysis—0
Syntactic Fusion: Enhancing Aspect-Level Sentiment Analysis Through Multi-Tree Graph Integration—0
Natural Language Processing Through Transfer Learning: A Case Study on Sentiment Analysis—0
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis—0
BERT Goes Off-Topic: Investigating the Domain Transfer Challenge using Genre ClassificationCode0
Benchmarking Large Language Model Volatility—0
Real-Time Online Stock Forecasting Utilizing Integrated Quantitative and Qualitative AnalysisCode0
Local Convergence of Approximate Newton Method for Two Layer Nonlinear Regression—0
nlpBDpatriots at BLP-2023 Task 2: A Transfer Learning Approach to Bangla Sentiment Analysis—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data—0
Explaining high-dimensional text classifiers—0
Sentiment Analysis of Twitter Posts on Global Conflicts—0
Unsupervised Graph Attention Autoencoder for Attributed Networks using K-means Loss—0
LowResource at BLP-2023 Task 2: Leveraging BanglaBert for Low Resource Sentiment Analysis of Bangla LanguageCode0
Optimal Strategies to Perform Multilingual Analysis of Social Content for a Novel Dataset in the Tourism Domain—0
Adapt in Contexts: Retrieval-Augmented Domain Adaptation via In-Context Learning—0
A Comprehensive Review on Sentiment Analysis: Tasks, Approaches and Applications—0
Implementation of AI Deep Learning Algorithm For Multi-Modal Sentiment Analysis—0
Unveiling Public Perceptions: Machine Learning-Based Sentiment Analysis of COVID-19 Vaccines in India—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