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 5401–5450 of 5630 papers

TitleStatusHype
Domain Adaptation of Transformer-Based Models using Unlabeled Data for Relevance and Polarity Classification of German Customer Feedback—0
Domain Adaptation Using a Combination of Multiple Embeddings for Sentiment Analysis—0
Domain Adaptation via Anaomaly Detection—0
DOMAIN ADAPTATION VIA DISTRIBUTION AND REPRESENTATION MATCHING: A CASE STUDY ON TRAINING DATA SELECTION VIA REINFORCEMENT LEARNING—0
Domain Adaptation with Category Attention Network for Deep Sentiment Analysis—0
Domain Aggregation Networks for Multi-Source Domain Adaptation—0
Domain and Task-Informed Sample Selection for Cross-Domain Target-based Sentiment Analysis—0
領域相關詞彙極性分析及文件情緒分類之研究 (Domain Dependent Word Polarity Analysis for Sentiment Classification) [In Chinese]—0
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis—0
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification—0
Domain-level Pairwise Semantic Interaction for Aspect-Based Sentiment Classification—0
Domain Sentiment Matters: A Two Stage Sentiment Analyzer—0
Domain-specific long text classification from sparse relevant information—0
Domain Specific Named Entity Recognition Referring to the Real World by Deep Neural Networks—0
Domain-Specific Sentiment Lexicons Induced from Labeled Documents—0
Domain-Specific Sentiment Word Extraction by Seed Expansion and Pattern Generation—0
DomEx: Extraction of Sentiment Lexicons for Domains and Meta-Domains—0
Do Multi-Sense Embeddings Improve Natural Language Understanding?—0
Do Neighbours Help? An Exploration of Graph-based Algorithms for Cross-domain Sentiment Classification—0
Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text—0
Do Weibo platform experts perform better at predicting stock market?—0
Do We Really Need Lexical Information? Towards a Top-down Approach to Sentiment Analysis of Product Reviews—0
DravidianMultiModality: A Dataset for Multi-modal Sentiment Analysis in Tamil and Malayalam—0
Drift to Remember—0
Drug Recommendation System based on Sentiment Analysis of Drug Reviews using Machine Learning—0
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach—0
DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter—0
Dual Adversarial Co-Learning for Multi-Domain Text Classification—0
Dual-Attention Model for Aspect-Level Sentiment Classification—0
DualCoTs: Dual Chain-of-Thoughts Prompting for Sentiment Lexicon Expansion of Idioms—0
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction—0
DualKanbaFormer: An Efficient Selective Sparse Framework for Multimodal Aspect-based Sentiment Analysis—0
Dual Memory Network Model for Biased Product Review Classification—0
Dual Supervised Learning—0
Dual Training and Dual Prediction for Polarity Classification—0
多目标情感分类中文数据集构建及分析研究(Construction and Analysis of Chinese Multi-Target Sentiment Classification Dataset)—0
DUTH at SemEval-2017 Task 4: A Voting Classification Approach for Twitter Sentiment Analysis—0
DUTH at SemEval-2017 Task 5: Sentiment Predictability in Financial Microblogging and News Articles—0
Dynamic Adaptive Optimization for Effective Sentiment Analysis Fine-Tuning on Large Language Models—0
Dynamic Adaptive Rank Space Exploration for Efficient Sentiment Analysis with Large Language Models—0
Dynamic Allocation of Crowd Contributions for Sentiment Analysis during the 2016 U.S. Presidential Election—0
Dynamic and Multi-Channel Graph Convolutional Networks for Aspect-Based Sentiment Analysis—0
Dynamic Backdoors with Global Average Pooling—0
Dynamic Cell Structure via Recursive-Recurrent Neural Networks—0
Dynamic Domain Information Modulation Algorithm for Multi-domain Sentiment Analysis—0
Dynamic Hedging Strategies in Derivatives Markets with LLM-Driven Sentiment and News Analytics—0
Dynamic Multimodal Sentiment Analysis: Leveraging Cross-Modal Attention for Enabled Classification—0
Dynamic Order Template Prediction for Generative Aspect-Based Sentiment Analysis—0
Dynamic Sentiment Analysis with Local Large Language Models using Majority Voting: A Study on Factors Affecting Restaurant Evaluation—0
Earlier Attention? Aspect-Aware LSTM for Aspect-Based 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