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 2401–2450 of 5630 papers

TitleStatusHype
On the logistical difficulties and findings of Jopara Sentiment AnalysisCode0
Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media—0
A Survey on sentiment analysis in Persian: A Comprehensive System Perspective Covering Challenges and Advances in Resources, and Methods—0
Weakly-supervised Multi-task Learning for Multimodal Affect Recognition—0
Interventional Aspect-Based Sentiment Analysis—0
Sentiment Classification in Swahili Language Using Multilingual BERT—0
Variational Weakly Supervised Sentiment Analysis with Posterior RegularizationCode0
Learning to Share by Masking the Non-shared for Multi-domain Sentiment Classification—0
Word2rate: training and evaluating multiple word embeddings as statistical transitions—0
Citations are not opinions: a corpus linguistics approach to understanding how citations are madeCode0
A Dual-Questioning Attention Network for Emotion-Cause Pair Extraction with Context AwarenessCode0
SINA-BERT: A pre-trained Language Model for Analysis of Medical Texts in Persian—0
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation—0
UDALM: Unsupervised Domain Adaptation through Language ModelingCode0
MeToo Tweets Sentiment Analysis Using Multi Modal frameworks—0
Unsupervised Learning of Explainable Parse Trees for Improved GeneralisationCode0
Sentiment-based Candidate Selection for NMTCode0
Exploring the Relationship Between Algorithm Performance, Vocabulary, and Run-Time in Text ClassificationCode0
Social contagion and asset prices: Reddit's self-organised bull runs—0
COVID-19 sentiment analysis via deep learning during the rise of novel cases—0
SetConv: A New Approach for Learning from Imbalanced Data—0
ONE: Toward ONE model, ONE algorithm, ONE corpus dedicated to sentiment analysis of Arabic/Arabizi and its dialects—0
Sentiment Analysis of Dravidian Code Mixed Data—0
Leveraging Offensive Language for Sarcasm and Sentiment Detection in Arabic—0
Task-Specific Pre-Training and Cross Lingual Transfer for Sentiment Analysis in Dravidian Code-Switched Languages—0
A COVID-19 news coverage mood map of Europe—0
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation—0
A New View of Multi-modal Language Analysis: Audio and Video Features as Text ``Styles''—0
DLRG@DravidianLangTech-EACL2021: Transformer based approachfor Offensive Language Identification on Code-Mixed Tamil—0
Pseudo-Label Guided Unsupervised Domain Adaptation of Contextual Embeddings—0
DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in Arabic—0
Is ``hot pizza'' Positive or Negative? Mining Target-aware Sentiment Lexicons—0
Creating and Evaluating Resources for Sentiment Analysis in the Low-resource Language: Sindhi—0
The IDC System for Sentiment Classification and Sarcasm Detection in Arabic—0
Predicting Treatment Outcome from Patient Texts:The Case of Internet-Based Cognitive Behavioural Therapy—0
Multi-task Learning Using a Combination of Contextualised and Static Word Embeddings for Arabic Sarcasm Detection and Sentiment Analysis—0
Introducing A large Tunisian Arabizi Dialectal Dataset for Sentiment Analysis—0
The Chinese Remainder Theorem for Compact, Task-Precise, Efficient and Secure Word Embeddings—0
Graph Convolutional Networks with Multi-headed Attention for Code-Mixed Sentiment Analysis—0
GEPSA, a tool for monitoring social challenges in digital press—0
Clustering Word Embeddings with Self-Organizing Maps. Application on LaRoSeDa - A Large Romanian Sentiment Data Set—0
Sentiment Classification of Code-Mixed Tweets using Bi-Directional RNN and Language Tags—0
InterpreT: An Interactive Visualization Tool for Interpreting Transformers—0
iCompass at Shared Task on Sarcasm and Sentiment Detection in Arabic—0
Lightweight Models for Multimodal Sequential Data—0
Exploring Implicit Sentiment Evoked by Fine-grained News Events—0
Arabic Emoji Sentiment Lexicon (Arab-ESL): A Comparison between Arabic and European Emoji Sentiment Lexicons—0
Machine Learning-Based Model for Sentiment and Sarcasm Detection—0
Retraining DistilBERT for a Voice Shopping Assistant by Using Universal Dependencies—0
Data Augmentation in a Hybrid Approach for Aspect-Based Sentiment AnalysisCode0
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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