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 3401–3450 of 5630 papers

TitleStatusHype
Implicit Sentiment Analysis with Event-centered Text Representation—0
Implicit Syntactic Features for Target-dependent Sentiment Analysis—0
Importance of Self-Attention for Sentiment Analysis—0
Improved Sentence Modeling using Suffix Bidirectional LSTM—0
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model—0
Improved Twitter Sentiment Prediction through Cluster-then-Predict Model—0
Improved Word Embeddings with Implicit Structure Information—0
Improvement and Implementation of a Speech Emotion Recognition Model Based on Dual-Layer LSTM—0
Improvement of a Naive Bayes Sentiment Classifier Using MRS-Based Features—0
Improve Sentiment Analysis of Citations with Author Modelling—0
Improving Aspect-based Sentiment Analysis with Gated Graph Convolutional Networks and Syntax-based Regulation—0
Improving Aspect-Level Sentiment Analysis with Aspect Extraction—0
Improving Bangla Linguistics: Advanced LSTM, Bi-LSTM, and Seq2Seq Models for Translating Sylheti to Modern Bangla—0
Improving Bi-LSTM Performance for Indonesian Sentiment Analysis Using Paragraph Vector—0
Improving Citation Polarity Classification with Product Reviews—0
Improving Claim Stance Classification with Lexical Knowledge Expansion and Context Utilization—0
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning—0
Improving Classifier Robustness through Active Generation of Pairwise Counterfactuals—0
Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density—0
Improving Distributed Representations of Tweets - Present and Future—0
Improving Distributed Representations of Tweets - Present and Future—0
Improving Document-Level Sentiment Classification Using Importance of Sentences—0
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories—0
Improving Few-Shot Performance of Language Models via Nearest Neighbor Calibration—0
Improving Formality Style Transfer with Context-Aware Rule Injection—0
Improving Minor Opinion Polarity Classification with Named Entity Analysis (L'apport des Entit\'es Nomm\'ees pour la classification des opinions minoritaires) [in French]—0
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network—0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge—0
Improving Multimodal Accuracy Through Modality Pre-training and Attention—0
Improving Multimodal fusion via Mutual Dependency Maximisation—0
Improving Multimodal Sentiment Analysis: Supervised Angular Margin-based Contrastive Learning for Enhanced Fusion Representation—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Improving Opinion-Target Extraction with Character-Level Word Embeddings—0
Improving Sentiment Analysis in Arabic Using Word Representation—0
Improving Sentiment Analysis in Twitter Using Multilingual Machine Translated Data—0
Improving Sentiment Analysis over non-English Tweets using Multilingual Transformers and Automatic Translation for Data-Augmentation—0
Improving Sentiment Analysis with Biofeedback Data—0
Improving Sentiment Classification Using 0-Shot Generated Labels for Custom Transformer Embeddings—0
Improving social relationships in face-to-face human-agent interactions: when the agent wants to know user's likes and dislikes—0
Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Improving the Out-Of-Distribution Generalization Capability of Language Models: Counterfactually-Augmented Data is not Enough—0
Improving the results of string kernels in sentiment analysis and Arabic dialect identification by adapting them to your test set—0
Improving Twitter Community Detection through Contextual Sentiment Analysis—0
Improving Twitter Named Entity Recognition using Word Representations—0
Improving Twitter Sentiment Analysis with Topic-Based Mixture Modeling and Semi-Supervised Training—0
Improving Twitter Sentiment Classification via Multi-Level Sentiment-Enriched Word Embeddings—0
Improving Unimodal Inference with Multimodal Transformers—0
Improving usual Naive Bayes classifier performances with Neural Naive Bayes based models—0
IMS at EmoInt-2017: Emotion Intensity Prediction with Affective Norms, Automatically Extended Resources and Deep Learning—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
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