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 1751–1800 of 5630 papers

TitleStatusHype
PQLM -- Multilingual Decentralized Portable Quantum Language Model for Privacy Protection—0
Improving Sentiment Analysis By Emotion Lexicon Approach on Vietnamese TextsCode0
Sentiment Analysis of ESG disclosures on Stock Market—0
SSP-Based Construction of Evaluation-Annotated Data for Fine-Grained Aspect-Based Sentiment Analysis—0
BRCC and SentiBahasaRojak: The First Bahasa Rojak Corpus for Pretraining and Sentiment Analysis Dataset—0
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction GenreCode0
面向情感分析的汉语构式语料库构建与应用研究—对汉语构式情感分析问题的思考(A Study of Chinese Construction Corpus Compilation and Application for Sentiment Analysis: A Discussion of Sentiment)—0
Biographically Relevant Tweets – a New Dataset, Linguistic Analysis and Classification Experiments—0
标签先验知识增强的方面类别情感分析方法研究(Aspect-Category based Sentiment Analysis Enhanced by Label Prior Knowledge)—0
PoliSe: Reinforcing Politeness Using User Sentiment for Customer Care Response Generation—0
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis—0
Fraunhofer SIT@SMM4H’22: Learning to Predict Stances and Premises in Tweets related to COVID-19 Health Orders Using Generative Models—0
Automatic Identification of 5C Vaccine Behaviour on Social Media—0
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment ClassificationCode0
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis—0
基于异构用户知识融合的隐式情感分析研究(Research on Implicit Sentiment Analysis based on Heterogeneous User Knowledge Fusion)—0
Measuring Robustness for NLP—0
Analyzing Coreference and Bridging in Product Reviews—0
Emotion Enriched Retrofitted Word Embeddings—0
Modeling Intra- and Inter-Modal Relations: Hierarchical Graph Contrastive Learning for Multimodal Sentiment Analysis—0
Span Extraction Aided Improved Code-mixed Sentiment ClassificationCode0
Leveraging Semantic and Sentiment Knowledge for User-Generated Text Sentiment Classification—0
Longitudinal Sentiment Analyses for Radicalization Research: Intertemporal Dynamics on Social Media Platforms and their Implications—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment ClassificationCode0
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance—0
Aspect-based Sentiment Analysis as Machine Reading Comprehension—0
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context FeaturesCode0
Aligning Multilingual Embeddings for Improved Code-switched Natural Language UnderstandingCode0
Can Data Diversity Enhance Learning Generalization?—0
Sentiment Interpretable Logic Tensor Network for Aspect-Term Sentiment Analysis—0
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis—0
A Survey: Credit Sentiment Score Prediction—0
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets—0
Lex2Sent: A bagging approach to unsupervised sentiment analysisCode0
Sentiment Analysis on Inflation after Covid-19—0
Adaptation of domain-specific transformer models with text oversampling for sentiment analysis of social media posts on Covid-19 vaccinesCode0
Text Revealer: Private Text Reconstruction via Model Inversion Attacks against Transformers—0
Linear Transformations for Cross-lingual Sentiment AnalysisCode0
Sequence Learning Using Equilibrium PropagationCode0
Computational Sarcasm Analysis on Social Media: A Systematic Review—0
emojiSpace: Spatial Representation of EmojisCode0
ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques—0
Does Attention Mechanism Possess the Feature of Human Reading? A Perspective of Sentiment Classification Task—0
A multiclass Q-NLP sentiment analysis experiment using DisCoCat—0
Zero-shot Aspect-level Sentiment Classification via Explicit Utilization of Aspect-to-Document Sentiment Composition—0
Quantitative Stopword Generation for Sentiment Analysis via Recursive and Iterative Deletion—0
Domain Adaptation from ScratchCode0
Sentiment Analysis on Twitter for the Major German Parties during the 2021 German Federal Election—0
The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach—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