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 4001–4050 of 5630 papers

TitleStatusHype
Multilevel sentiment analysis in arabic—0
Multi-Level Sentiment Analysis of PolEmo 2.0: Extended Corpus of Multi-Domain Consumer Reviews—0
Multilingual Affect Polarity and Valence Prediction in Metaphors—0
Multilingual Connotation Frames: A Case Study on Social Media for Targeted Sentiment Analysis and Forecast—0
Multilingual Few-Shot Learning via Language Model Retrieval—0
Multilingual Large Language Models Are Not (Yet) Code-Switchers—0
Multilingual Model Using Cross-Task Embedding Projection—0
Multilingual Sentiment Analysis: An RNN-Based Framework for Limited Data—0
Multi-Lingual Sentiment Analysis of Social Data Based on Emotion-Bearing Patterns—0
Multilingual Sentiment Analysis using Machine Translation?—0
Multilingual Subjectivity and Sentiment Analysis—0
Multilingual Training of Crosslingual Word Embeddings—0
Multilingual WSD with Just a Few Lines of Code: the BabelNet API—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Multimodal Deep Reinforcement Learning for Portfolio Optimization—0
Multimodal Emotion Recognition and Sentiment Analysis in Multi-Party Conversation Contexts—0
Multimodal Feature Extraction for Memes Sentiment Classification—0
Multimodal fusion via cortical network inspired losses—0
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition—0
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph—0
Multimodal Mixture of Low-Rank Experts for Sentiment Analysis and Emotion Recognition—0
Multimodal Relational Tensor Network for Sentiment and Emotion Classification—0
Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis—0
Multimodal Sentiment Analysis—0
Multimodal Sentiment Analysis: Addressing Key Issues and Setting up the Baselines—0
Multimodal Sentiment Analysis: A Survey—0
Multimodal Sentiment Analysis Based on BERT and ResNet—0
Multimodal Sentiment Analysis Based on Causal Reasoning—0
Multimodal Sentiment Analysis based on Video and Audio Inputs—0
Multimodal Sentiment Analysis on CMU-MOSEI Dataset using Transformer-based Models—0
Multimodal Sentiment Analysis: Perceived vs Induced Sentiments—0
Multi-modal Sentiment Analysis using Deep Canonical Correlation Analysis—0
Multi-modal Sentiment Analysis using Super Characters Method on Low-power CNN Accelerator Device—0
Multimodal Sentiment Analysis with Common-sense Modulation—0
Multimodal Sentiment Analysis with Missing Modality: A Knowledge-Transfer Approach—0
Multimodal Sentiment Analysis with Multi-perspective Fusion Network Focusing on Sense Attentive Language—0
Multimodal sparse representation learning and applications—0
Multimodal Stock Price Prediction—0
Multimodal Transformers are Hierarchical Modal-wise Heterogeneous Graphs—0
Multiple-element joint detection for Aspect-Based Sentiment Analysis—0
Multiple-Source Adaptation for Regression Problems—0
Multiple Source Domain Adaptation with Adversarial Learning—0
Multiple views as aid to linguistic annotation error analysis—0
Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining—0
Multiplicative Tree-Structured Long Short-Term Memory Networks for Semantic Representations—0
Multi-Purpose NLP Chatbot : Design, Methodology & Conclusion—0
Multi-Scale and Multi-Objective Optimization for Cross-Lingual Aspect-Based Sentiment Analysis—0
Multi-scale Cooperative Multimodal Transformers for Multimodal Sentiment Analysis in Videos—0
Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits—0
Multi-source Domain Adaptation for Visual Sentiment Classification—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