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 2251–2300 of 5630 papers

TitleStatusHype
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis—0
A Comprehensive Overview of Recommender System and Sentiment Analysis—0
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis—0
Deep Learning Models for Sentiment Analysis in Arabic—0
Attention-based Recurrent Convolutional Neural Network for Automatic Essay Scoring—0
Deep Learning for Sentiment Analysis - Invited Talk—0
Attention-based LSTM Network for Cross-Lingual Sentiment Classification—0
Analyzing Urdu Social Media for Sentiments using Transfer Learning with Controlled Translations—0
Deep Learning for NLP (without Magic)—0
Deep Learning for Digital Text Analytics: Sentiment Analysis—0
Analyzing the Impact of Sentiments of Scientific Articles on COVID-19 Vaccination Rates—0
AdvCodeMix: Adversarial Attack on Code-Mixed Data—0
Deep Learning for Climate Action: Computer Vision Analysis of Visual Narratives on X—0
Deep learning for affective computing: text-based emotion recognition in decision support—0
Deep Learning Brasil - NLP at SemEval-2020 Task 9: Sentiment Analysis of Code-Mixed Tweets Using Ensemble of Language Models—0
Deep Learning Brasil -- NLP at SemEval-2020 Task 9: Overview of Sentiment Analysis of Code-Mixed Tweets—0
Deep Learning-based Sentiment Classification: A Comparative Survey—0
Deep Learning-based Sentiment Analysis of Olympics Tweets—0
A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives—0
Analyzing the Generalizability of Deep Contextualized Language Representations For Text Classification—0
A Comprehensive Evaluation of Large Language Models on Aspect-Based Sentiment Analysis—0
ABSA-Bench: Towards the Unified Evaluation of Aspect-based Sentiment Analysis Research—0
Deep Learning-based Sentiment Analysis in Persian Language—0
Deep learning based Chinese text sentiment mining and stock market correlation research—0
Deep Learning applications for COVID-19—0
ATP: A holistic attention integrated approach to enhance ABSA—0
Analyzing Sentiment Word Relations with Affect, Judgment, and Appreciation—0
DeepHider: A Covert NLP Watermarking Framework Based on Multi-task Learning—0
A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon—0
Deep Discriminative Learning for Unsupervised Domain Adaptation—0
Analyzing Sentiment in Classical Chinese Poetry—0
AdvCodec: Towards A Unified Framework for Adversarial Text Generation—0
Deep Convolutional Neural Network Textual Features and Multiple Kernel Learning for Utterance-level Multimodal Sentiment Analysis—0
Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts—0
Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis—0
A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities—0
Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets—0
DeepBlueAI at WANLP-EACL2021 task 2: A Deep Ensemble-based Method for Sarcasm and Sentiment Detection in Arabic—0
Deep Bayesian Natural Language Processing—0
Deep Bayesian Learning and Understanding—0
Analyzing Political Parody in Social Media—0
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT—0
A comprehensive cross-language framework for harmful content detection with the aid of sentiment analysis—0
Deep Automated Multi-task Learning—0
Decoding Visual Sentiment of Political Imagery—0
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information—0
Decoding EEG Brain Activity for Multi-Modal Natural Language Processing—0
Decision Tree J48 at SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text (Hinglish)—0
A Text-Image Pair Is not Enough: Language-Vision Relation Inference with Auxiliary Modality Translation—0
Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis—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