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 38013850 of 5630 papers

TitleStatusHype
Distribution of Emotional Reactions to News Articles in Twitter0
Introducing a Lexicon of Verbal Polarity Shifters for EnglishCode0
Joint Learning of Sense and Word Embeddings0
Resource Creation Towards Automated Sentiment Analysis in Telugu (a low resource language) and Integrating Multiple Domain Sources to Enhance Sentiment Prediction0
A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language0
SentiArabic: A Sentiment Analyzer for Standard Arabic0
Medical Entity Corpus with PICO elements and Sentiment Analysis0
Retrofitting Word Representations for Unsupervised Sense Aware Word Similarities0
Sample-to-Sample Correspondence for Unsupervised Domain Adaptation0
"I ain't tellin' white folks nuthin": A quantitative exploration of the race-related problem of candour in the WPA slave narratives0
Word2Vec and Doc2Vec in Unsupervised Sentiment Analysis of Clinical Discharge Summaries0
Recurrent Entity Networks with Delayed Memory Update for Targeted Aspect-based Sentiment AnalysisCode0
Hierarchical Attention Transfer Network for Cross-Domain Sentiment ClassificationCode0
Strong Baselines for Neural Semi-supervised Learning under Domain ShiftCode0
Generating Natural Language Adversarial ExamplesCode0
Stylistic Variation in Social Media Part-of-Speech Tagging0
Rafiki: Machine Learning as an Analytics Service SystemCode0
Predicting Cyber Events by Leveraging Hacker Sentiment0
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification0
Deep Learning for Digital Text Analytics: Sentiment Analysis0
Automated Classification of Text Sentiment0
Crowd-Labeling Fashion Reviews with Quality ControlCode0
Emotions are Universal: Learning Sentiment Based Representations of Resource-Poor Languages using Siamese Networks0
Sentiment Analysis of Code-Mixed Languages leveraging Resource Rich LanguagesCode0
Automatic Normalization of Word Variations in Code-Mixed Social Media Text0
Real Time Sentiment Change Detection of Twitter Data Streams0
Financial Aspect and Sentiment Predictions with Deep Neural Networks: An Ensemble Approach0
Targeted Aspect-Based Sentiment Analysis via Embedding Commonsense Knowledge into an Attentive LSTM0
Learning Latent Opinions for Aspect-Level Sentiment ClassificationCode0
Automatically augmenting an emotion dataset improves classification using audio0
Investigating Capsule Networks with Dynamic Routing for Text ClassificationCode0
Universal Sentence EncoderCode1
A Web Scraping Methodology for Bypassing Twitter API Restrictions0
Near-lossless Binarization of Word EmbeddingsCode0
Stance Detection on Tweets: An SVM-based Approach0
MultiBooked: A Corpus of Basque and Catalan Hotel Reviews Annotated for Aspect-level Sentiment Classification0
Sentiment Analysis of Comments on Rohingya Movement with Support Vector Machine0
Contextual Salience for Fast and Accurate Sentence VectorsCode0
ρ-hot Lexicon Embedding-based Two-level LSTM for Sentiment AnalysisCode0
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines0
Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-20170
Corpus Statistics in Text Classification of Online Data0
Deep learning for affective computing: text-based emotion recognition in decision support0
How to evaluate sentiment classifiers for Twitter time-ordered data?0
Preparing Bengali-English Code-Mixed Corpus for Sentiment Analysis of Indian Languages0
Learning Rules-First Classifiers0
Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment ClassificationCode0
Neural Monkey: The Current State and Beyond0
Balancing Translation Quality and Sentiment Preservation (Non-archival Extended Abstract)0
Improving Sentiment Analysis in Arabic Using Word Representation0
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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