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 2901–2950 of 5630 papers

TitleStatusHype
Manovaad: A Novel Approach to Event Oriented Corpus Creation Capturing Subjectivity and Focus—0
Comparing Machine Learning and Deep Learning Approaches on NLP Tasks for the Italian Language—0
Target-based Sentiment Annotation in Chinese Financial News—0
Evaluating Word Embeddings for Indonesian--English Code-Mixed Text Based on Synthetic Data—0
Creating a Sentiment Lexicon with Game-Specific Words for Analyzing NPC Dialogue in The Elder Scrolls V: Skyrim—0
Does History Matter? Using Narrative Context to Predict the Trajectory of Sentence Sentiment—0
Affection Driven Neural Networks for Sentiment Analysis—0
Aspect On: an Interactive Solution for Post-Editing the Aspect Extraction based on Online Learning—0
From Arabic Sentiment Analysis to Sarcasm Detection: The ArSarcasm Dataset—0
Objective Assessment of Subjective Tasks in Crowdsourcing Applications—0
Odi et Amo. Creating, Evaluating and Extending Sentiment Lexicons for Latin.—0
An Arabic Tweets Sentiment Analysis Dataset (ATSAD) using Distant Supervision and Self Training—0
Offensive Language Detection Using Brown Clustering—0
Urban Dictionary Embeddings for Slang NLP Applications—0
An Annotation Framework for Luxembourgish Sentiment Analysis—0
Email Classification Incorporating Social Networks and Thread Structure—0
Ellogon Casual Annotation Infrastructure—0
Optimizing Annotation Effort Using Active Learning Strategies: A Sentiment Analysis Case Study in Persian—0
A Term Extraction Approach to Survey Analysis in Health Care—0
Towards a Multi-Dataset for Complex Emotions Learning Based on Deep Neural Networks—0
Information Space Dashboard—0
Metaphorical Expressions in Automatic Arabic Sentiment Analysis—0
Analyzing ELMo and DistilBERT on Socio-political News Classification—0
Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis—0
Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERTCode0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Detecting Domain Polarity-Changes of Words in a Sentiment Lexicon—0
Analyzing Political Parody in Social Media—0
Octa: Omissions and Conflicts in Target-Aspect Sentiment Analysis—0
GLUECoS : An Evaluation Benchmark for Code-Switched NLP—0
CrowdTSC: Crowd-based Neural Networks for Text Sentiment Classification—0
Survey on Visual Sentiment Analysis—0
Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach—0
Development of a General Purpose Sentiment Lexicon for Igbo Language—0
In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19—0
A Deep Learning System for Sentiment Analysis of Service Calls—0
Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision—0
A Hybrid Approach for Aspect-Based Sentiment Analysis Using Deep Contextual Word Embeddings and Hierarchical AttentionCode0
Enhancing Pharmacovigilance with Drug Reviews and Social MediaCode0
LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanation—0
Sentiment Analysis of Yelp Reviews: A Comparison of Techniques and ModelsCode0
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis:Findings of A 20 Domain Study—0
Word frequency and sentiment analysis of twitter messages during Coronavirus pandemic—0
Pruning and Sparsemax Methods for Hierarchical Attention NetworksCode0
Neutralizing Gender Bias in Word Embedding with Latent Disentanglement and Counterfactual Generation—0
Teacher-Class Network: A Neural Network Compression MechanismCode0
Increasing the Inference and Learning Speed of Tsetlin Machines with Clause Indexing—0
Predicting Strategic Behavior from Free TextCode0
Enhancing Review Comprehension with Domain-Specific Commonsense—0
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment AnalysisCode0
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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