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 2701–2750 of 5630 papers

TitleStatusHype
TextDecepter: Hard Label Black Box Attack on Text Classification—0
Evaluating NLP Models via Contrast Sets—0
Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language—0
Better Queries for Aspect-Category Sentiment Classification—0
多目标情感分类中文数据集构建及分析研究(Construction and Analysis of Chinese Multi-Target Sentiment Classification Dataset)—0
Assessing Robustness of Text Classification through Maximal Safe Radius ComputationCode0
Aspect-based Sentiment Analysis on Indonesia’s Tourism Destinations Based on Google Maps User Code-Mixed Reviews (Study Case: Borobudur and Prambanan Temples)—0
Classification of Nostalgic Music Through LDA Topic Modeling and Sentiment Analysis of YouTube Comments in Japanese Songs—0
结合金融领域情感词典和注意力机制的细粒度情感分析(Attention-based Recurrent Network Combined with Financial Lexicon for Aspect-level Sentiment Classification)—0
基于层次注意力机制和门机制的属性级别情感分析(Aspect-level Sentiment Analysis Based on Hierarchical Attention and Gate Networks)—0
文本情感分析中的重叠现象研究(A Study on Repetition in Text-based Sentiment Analysis)—0
LEBANONUPRISING: a thorough study of Lebanese tweets—0
Domain Adversarial Fine-Tuning as an Effective RegularizerCode0
Quantal synaptic dilution enhances sparse encoding and dropout regularisation in deep networks—0
Metaphor Detection using Deep Contextualized Word Embeddings—0
Empirical Study of Text Augmentation on Social Media Text in VietnameseCode0
Automatic Extraction of Agriculture Terms from Domain Text: A Survey of Tools and Techniques—0
WESSA at SemEval-2020 Task 9: Code-Mixed Sentiment Analysis using Transformers—0
Subjective Metrics-based Cloud Market Performance Prediction—0
An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management—0
Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations—0
An Interpretable and Uncertainty Aware Multi-Task Framework for Multi-Aspect Sentiment AnalysisCode0
Arabic Opinion Mining Using a Hybrid Recommender System Approach—0
Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector—0
Pay Attention when RequiredCode0
Regularised Text Logistic Regression: Key Word Detection and Sentiment Classification for Online Reviews—0
kk2018 at SemEval-2020 Task 9: Adversarial Training for Code-Mixing Sentiment Classification—0
E-BERT: A Phrase and Product Knowledge Enhanced Language Model for E-commerce—0
TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis—0
NLP-CIC at SemEval-2020 Task 9: Analysing sentiment in code-switching language using a simple deep-learning classifier—0
UPB at SemEval-2020 Task 9: Identifying Sentiment in Code-Mixed Social Media Texts using Transformers and Multi-Task Learning—0
Visual Sentiment Analysis from Disaster Images in Social Media—0
Sentiment Analysis for Investment Atmosphere Scoring—0
Aspect-Based Sentiment Analysis Based on BERT-DAOA—0
LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment AnalysisCode0
A Decade of In-text Citation Analysis based on Natural Language Processing and Machine Learning Techniques: An overview of empirical studies—0
Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic—0
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)—0
Multi-Label Sentiment Analysis on 100 Languages with Dynamic Weighting for Label ImbalanceCode0
SHAP values for Explaining CNN-based Text Classification Models—0
Many-to-one Recurrent Neural Network for Session-based Recommendation—0
Simple Unsupervised Similarity-Based Aspect ExtractionCode0
The Impact of Indirect Machine Translation on Sentiment Classification—0
Learning from students' perception on professors through opinion mining—0
Two Stages Approach for Tweet Engagement Prediction—0
Cross-Cultural Polarity and Emotion Detection Using Sentiment Analysis and Deep Learning -- a Case Study on COVID-19—0
Turkish Text Classification: From Lexicon Analysis to Bidirectional Transformer—0
A Variational Approach to Unsupervised Sentiment Analysis—0
SentiQ: A Probabilistic Logic Approach to Enhance Sentiment Analysis Tool Quality—0
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification—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