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 1951–2000 of 5630 papers

TitleStatusHype
DMCB at SemEval-2018 Task 1: Transfer Learning of Sentiment Classification Using Group LSTM for Emotion Intensity prediction—0
AutoTestForge: A Multidimensional Automated Testing Framework for Natural Language Processing Models—0
Enhancing Review Comprehension with Domain-Specific Commonsense—0
DLRG@DravidianLangTech-EACL2021: Transformer based approachfor Offensive Language Identification on Code-Mixed Tamil—0
Enhancing Robustness in Aspect-based Sentiment Analysis by Better Exploiting Data Augmentation—0
Enhancing Root Extractors Using Light Stemmers—0
Enhancing search engine precision and user experience through sentiment-based polysemy resolution—0
DLIREC: Aspect Term Extraction and Term Polarity Classification System—0
Automatic word alignment tools to scale production of manually aligned parallel texts—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies—0
A Neural Network for Factoid Question Answering over Paragraphs—0
Diversified Multiple Instance Learning for Document-Level Multi-Aspect Sentiment Classification—0
Advancing Aspect-Based Sentiment Analysis through Deep Learning Models—0
Automatic Triage of Mental Health Forum Posts—0
Enhancing Zero-Shot Crypto Sentiment with Fine-tuned Language Model and Prompt Engineering—0
Dive deeper: Deep Semantics for Sentiment Analysis—0
Ensemble BERT: A student social network text sentiment classification model based on ensemble learning and BERT architecture—0
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning—0
Ensemble Language Models for Multilingual Sentiment Analysis—0
An Ensemble of Humour, Sarcasm, and Hate Speechfor Sentiment Classification in Online Reviews—0
Ensemble Technique Utilization for Indonesian Dependency Parser—0
Distribution of Emotional Reactions to News Articles in Twitter—0
Entertainment chatbot for the digital inclusion of elderly people without abstraction capabilities—0
Automatic Sarcasm Detection: A Survey—0
Entity-Aspect-Opinion-Sentiment Quadruple Extraction for Fine-grained Sentiment Analysis—0
Entity-Aware Biaffine Attention Model for Improved Constituent Parsing with Reduced Entity Violations—0
Entity Centric Opinion Mining from Blogs—0
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation—0
Entity/Event-Level Sentiment Detection and Inference—0
Entity Hierarchy Embedding—0
Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models—0
Automatic Rule Extraction from Long Short Term Memory Networks—0
An Ensemble Model for Sentiment Analysis of Hindi-English Code-Mixed Data—0
Adverse Media Mining for KYC and ESG Compliance—0
Entity Linking on Microblogs with Spatial and Temporal Signals—0
A Concrete Chinese NLP Pipeline—0
Entity-Specific Sentiment Classification of Yahoo News Comments—0
A Case Study of Chinese Sentiment Analysis on Social Media Reviews Based on LSTM—0
Epita at SemEval-2018 Task 1: Sentiment Analysis Using Transfer Learning Approach—0
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption—0
Equity Beyond Bias in Language Technologies for Education—0
ERAS: Evaluating the Robustness of Chinese NLP Models to Morphological Garden Path Errors—0
Tracking Emotional Dynamics in Chat Conversations: A Hybrid Approach using DistilBERT and Emoji Sentiment Analysis—0
Distributed Representations for Unsupervised Semantic Role Labeling—0
Distributed Real-Time Sentiment Analysis for Big Data Social Streams—0
Automatic Noun Compound Interpretation using Deep Neural Networks and Word Embeddings—0
ERUPD -- English to Roman Urdu Parallel Dataset—0
Distributed Deep Learning Using Volunteer Computing-Like Paradigm—0
Distinguishing Literal and Non-Literal Usage of German Particle Verbs—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