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

Showing 14011425 of 5630 papers

TitleStatusHype
Towards Exploiting Sticker for Multimodal Sentiment Analysis in Social Media: A New Dataset and BaselineCode1
Learnable Dependency-based Double Graph Structure for Aspect-based Sentiment Analysis0
Linguistically Motivated Features for Classifying Shorter Text into Fiction and Non-Fiction GenreCode0
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment ClassificationCode1
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis0
Composition-based Heterogeneous Graph Multi-channel Attention Network for Multi-aspect Multi-sentiment ClassificationCode0
Aspect-based Sentiment Analysis as Machine Reading Comprehension0
One-Teacher and Multiple-Student Knowledge Distillation on Sentiment ClassificationCode0
Aligning Multilingual Embeddings for Improved Code-switched Natural Language UnderstandingCode0
Unsupervised Data Augmentation for Aspect Based Sentiment Analysis0
Modeling Aspect Correlation for Aspect-based Sentiment Analysis via Recurrent Inverse Learning Guidance0
Can Data Diversity Enhance Learning Generalization?0
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis0
KC-ISA: An Implicit Sentiment Analysis Model Combining Knowledge Enhancement and Context FeaturesCode0
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
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
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
Longitudinal Sentiment Analyses for Radicalization Research: Intertemporal Dynamics on Social Media Platforms and their Implications0
A Survey: Credit Sentiment Score Prediction0
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets0
METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related TweetsCode1
WikiDes: A Wikipedia-Based Dataset for Generating Short Descriptions from ParagraphsCode1
Lex2Sent: A bagging approach to unsupervised sentiment analysisCode0
Sentiment Analysis on Inflation after Covid-190
Augmenting Interpretable Models with LLMs during TrainingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2MT-DNN-SMARTAccuracy97.5Unverified
3T5-11BAccuracy97.5Unverified
4MUPPET Roberta LargeAccuracy97.4Unverified
5T5-3BAccuracy97.4Unverified
6ALBERTAccuracy97.1Unverified
7StructBERTRoBERTa ensembleAccuracy97.1Unverified
8XLNet (single model)Accuracy97Unverified
9SMARTRoBERTaDev Accuracy96.9Unverified
10ELECTRAAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68Unverified
2RoBERTa-largeAccuracy96.54Unverified
3XLNetAccuracy96.21Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1Unverified
6GraphStarAccuracy96Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9Unverified
10BERT large finetune UDAAccuracy95.8Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07Unverified
6Heinsen Routing + GPT-2Accuracy58.5Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2Unverified
8BERT LargeAccuracy55.5Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9Unverified
10BCN+ELMoAccuracy54.7Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88Unverified
2SVDCNNError4.74Unverified
3LEAMError4.69Unverified
4fastText, h=10, bigramError4.3Unverified
5SWEM-hierError4.19Unverified
6SRNNError3.96Unverified
7M-ACNNError3.89Unverified
8DNC+CUWError3.6Unverified
9CCCapsNetError3.52Unverified
10Block-sparse LSTMError3.27Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500Unverified
2VLAWEAccuracy93.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5Unverified
4AnglE-LLaMA-7BAccuracy91.09Unverified
5byte mLSTM7Accuracy86.8Unverified
6MEANAccuracy84.5Unverified
7RNN-CapsuleAccuracy83.8Unverified
8Capsule-BAccuracy82.3Unverified
9SuBiLSTM-TiedAccuracy81.6Unverified
10USE_T+CNNAccuracy81.59Unverified