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 5201–5250 of 5630 papers

TitleStatusHype
CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding—0
Curating Stopwords in Marathi: A TF-IDF Approach for Improved Text Analysis and Information Retrieval—0
Current Landscape of the Russian Sentiment Corpora—0
Curriculum Learning Meets Weakly Supervised Modality Correlation Learning—0
Curriculum Learning Strategies for Hindi-English Codemixed Sentiment Analysis—0
Curse or Boon? Presence of Subjunctive Mood in Opinionated Text—0
Customer Sentiment Analysis using Weak Supervision for Customer-Agent Chat—0
Cyberbullying or just Sarcasm? Unmasking Coordinated Networks on Reddit—0
Cyclegen: Cyclic consistency based product review generator from attributes—0
CYUT at ROCLING-2021 Shared Task: Based on BERT and MacBERT—0
DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter—0
DAGA: Data Augmentation with a Generation Approach for Low-resource Tagging Tasks—0
DAG-Structured Long Short-Term Memory for Semantic Compositionality—0
DaMSTF: Domain Adversarial Learning Enhanced Meta Self-Training for Domain Adaptation—0
DASentimental: Detecting depression, anxiety and stress in texts via emotional recall, cognitive networks and machine learning—0
Data augmentation for low resource sentiment analysis using generative adversarial networks—0
Data Augmentation using Transformers and Similarity Measures for Improving Arabic Text Classification—0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN—0
Dataset and Baseline for Automatic Student Feedback Analysis—0
Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision—0
Dataset Creation and Evaluation of Aspect Based Sentiment Analysis in Telugu, a Low Resource Language—0
Data Set for Stance and Sentiment Analysis from User Comments on Croatian News—0
Dataset of Philippine Presidents Speeches from 1935 to 2016—0
Datasets for Aspect-Based Sentiment Analysis in French—0
Data Sets: Word Embeddings Learned from Tweets and General Data—0
Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment Analysis—0
DCR-Net: A Deep Co-Interactive Relation Network for Joint Dialog Act Recognition and Sentiment Classification—0
DCU: Aspect-based Polarity Classification for SemEval Task 4—0
DeBERTinha: A Multistep Approach to Adapt DebertaV3 XSmall for Brazilian Portuguese Natural Language Processing Task—0
Debunking Sentiment Lexicons: A Case of Domain-Specific Sentiment Classification for Croatian—0
Decision Making For Celebrity Branding: An Opinion Mining Approach Based On Polarity And Sentiment Analysis Using Twitter Consumer-Generated Content (CGC)—0
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)—0
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing—0
Decoding Visual Sentiment of Political Imagery—0
Deep Automated Multi-task Learning—0
Deep Bayesian Learning and Understanding—0
Deep Bayesian Natural Language Processing—0
DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in Arabic—0
Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis—0
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts—0
Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis—0
Deep Discriminative Learning for Unsupervised Domain Adaptation—0
DeepHider: A Covert NLP Watermarking Framework Based on Multi-task Learning—0
Deep Learning applications for COVID-19—0
Deep learning based Chinese text sentiment mining and stock market correlation research—0
Deep Learning-based Sentiment Analysis in Persian Language—0
Deep Learning-based Sentiment Analysis of Olympics Tweets—0
Deep Learning-based Sentiment Classification: A Comparative Survey—0
Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets—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