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 4251–4300 of 5630 papers

TitleStatusHype
A data and analysis resource for an experiment in text mining a collection of micro-blogs on a political topic.—0
A Database of Attribution Relations—0
A Data-driven Neural Network Architecture for Sentiment Analysis—0
A Dataset and Benchmarks for Multimedia Social Analysis—0
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts—0
A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection—0
Addition of Code Mixed Features to Enhance the Sentiment Prediction of Song Lyrics—0
A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies—0
A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews—0
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction—0
A Deep Features-Based Approach Using Modified ResNet50 and Gradient Boosting for Visual Sentiments Classification—0
A Deep Language-independent Network to analyze the impact of COVID-19 on the World via Sentiment Analysis—0
A Deep Learning Approach to Integrate Human-Level Understanding in a Chatbot—0
A deep-learning framework to detect sarcasm targets—0
A Deep Learning System for Sentiment Analysis of Service Calls—0
A deep Natural Language Inference predictor without language-specific training data—0
A Dependency Parser for Tweets—0
A Dictionary-Based Approach to Identifying Aspects Implied by Adjectives for Opinion Mining—0
A Distant Supervision Approach to Semantic Role Labeling—0
Adjective Intensity and Sentiment Analysis—0
A Dual-Module Denoising Approach with Curriculum Learning for Enhancing Multimodal Aspect-Based Sentiment Analysis—0
Adullam at SemEval-2017 Task 4: Sentiment Analyzer Using Lexicon Integrated Convolutional Neural Networks with Attention—0
Advanced Deep Learning Techniques for Analyzing Earnings Call Transcripts: Methodologies and Applications—0
Advances in Argument Mining—0
Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets—0
Advancing Fairness in Natural Language Processing: From Traditional Methods to Explainability—0
Advancing Humor-Focused Sentiment Analysis through Improved Contextualized Embeddings and Model Architecture—0
Advancing NLP Models with Strategic Text Augmentation: A Comprehensive Study of Augmentation Methods and Curriculum Strategies—0
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions—0
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT—0
AdvCodec: Towards A Unified Framework for Adversarial Text Generation—0
AdvCodeMix: Adversarial Attack on Code-Mixed Data—0
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis—0
Adversarial Attack on Sentiment Classification—0
Adversarial Attacks and Defense on Texts: A Survey—0
Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions—0
Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis—0
Adversarial Category Alignment Network for Cross-domain Sentiment Classification—0
Adversarial Evasion Attack Efficiency against Large Language Models—0
Adversarial Examples for Natural Language Classification Problems—0
Adversarial Multimodal Domain Transfer for Video-Level Sentiment Analysis—0
Adversarial Multiple Source Domain Adaptation—0
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis—0
Adversarial Training: A simple and efficient technique to Improving NLP Robustness—0
Adversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis—0
Adversarial Training in Affective Computing and Sentiment Analysis: Recent Advances and Perspectives—0
Adverse Media Mining for KYC and ESG Compliance—0
Aesthetic Visual Question Answering of Photographs—0
Aff2Vec: Affect--Enriched Distributional Word Representations—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—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