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 2001–2050 of 5630 papers

TitleStatusHype
Investigating the Impact of COVID-19 on Education by Social Network Mining—0
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection—0
HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model—0
Dynamic Backdoors with Global Average Pooling—0
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density EstimationCode0
TraceNet: Tracing and Locating the Key Elements in Sentiment AnalysisCode0
Commonsense Reasoning for Identifying and Understanding the Implicit Need of Help and Synthesizing Assistive Actions—0
Utilizing Out-Domain Datasets to Enhance Multi-Task Citation Analysis—0
Domain-level Pairwise Semantic Interaction for Aspect-Based Sentiment Classification—0
Aspect Based Sentiment Analysis Using Spectral Temporal Graph Neural Network—0
Using Random Perturbations to Mitigate Adversarial Attacks on Sentiment Analysis Models—0
Hindi/Bengali Sentiment Analysis Using Transfer Learning and Joint Dual Input Learning with Self AttentionCode0
Gender stereotypes in the mediated personalization of politics: Empirical evidence from a lexical, syntactic and sentiment analysis—0
Aspect-based Sentiment Analysis through EDU-level Attentions—0
Zero-Shot Aspect-Based Sentiment Analysis—0
Privacy-Aware Crowd Labelling for Machine Learning Tasks—0
L3Cube-MahaCorpus and MahaBERT: Marathi Monolingual Corpus, Marathi BERT Language Models, and Resources—0
Active Learning Over Multiple Domains in Natural Language Tasks—0
Negativity Spreads Faster: A Large-Scale Multilingual Twitter Analysis on the Role of Sentiment in Political CommunicationCode0
AI-based Approach for Safety Signals Detection from Social Networks: Application to the Levothyrox Scandal in 2017 on Doctissimo Forum—0
Does Transliteration Help Multilingual Language Modeling?Code0
A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment DatasetsCode0
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment AnalysisCode0
Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognition—0
Multi-channel Attentive Graph Convolutional Network With Sentiment Fusion For Multimodal Sentiment Analysis—0
Identifying Adversarial Attacks on Text Classifiers—0
Sentiment Analysis: Predicting Yelp Scores—0
Predicting The Stock Trend Using News Sentiment Analysis and Technical Indicators in SparkCode0
Small Cohort of Epilepsy Patients Showed Increased Activity on Facebook before Sudden Unexpected Death—0
A Study on the Ambiguity in Human Annotation of German Oral History Interviews for Perceived Emotion Recognition and Sentiment Analysis—0
A Deep Convolutional Neural Networks Based Multi-Task Ensemble Model for Aspect and Polarity Classification in Persian ReviewsCode0
Investigating the saliency of sentiment expressions in aspect-based sentiment analysis—0
A Holistic Framework for Analyzing the COVID-19 Vaccine Debate—0
Enhancing Robustness in Aspect-based Sentiment Analysis by Better Exploiting Data Augmentation—0
When More is not Necessary Better: Multilingual Auxiliary Tasks for Zero-Shot Cross-Lingual Transfer of Hate Speech Detection Models—0
Lexicon based Fine-tuning of Multilingual Language Models for Sentiment Analysis of Low-resource Languages—0
TraceNet: Tracing and Locating the Key Elements in Sentiment Analysis—0
Lex2Sent: A bagging approach to unsupervised sentiment analysis—0
Discriminative Models Can Still Outperform Generative Models in Aspect Based Sentiment Analysis—0
Detection of Word Adversarial Examples in NLP: Benchmark and Baseline via Robust Density EstimationCode0
STT: Soft Template Tuning for Few-Shot Learning—0
A Text-Image Pair Is not Enough: Language-Vision Relation Inference with Auxiliary Modality Translation—0
Analyzing Modality Robustness in Multimodal Sentiment Analysis—0
A Benchmark for Text Quantification Learning Under Real-World Temporal Distribution Shift—0
Challenge for open-domain targeted sentiment analysis—0
Machine Learning for Food Review and Recommendation—0
Large Scale Analysis of Open MOOC Reviews to Support Learners' Course Selection—0
Explaining Predictive Uncertainty by Looking Back at Model Explanations—0
Turkish Sentiment Analysis Using Machine Learning Methods: Application on Online Food Order Site Reviews—0
Sentiment Analysis with Deep Learning Models: A Comparative Study on a Decade of Sinhala Language Facebook Data—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