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 1251–1300 of 5630 papers

TitleStatusHype
Comparison of SVM Optimization Techniques in the Primal—0
Comprehensive Study on Sentiment Analysis: From Rule-based to modern LLM based system—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Computational Content Analysis of Negative Tweets for Obesity, Diet, Diabetes, and Exercise—0
Computational Sarcasm—0
Computational Sarcasm Analysis on Social Media: A Systematic Review—0
Computing Affect in Metaphors—0
Computing Sentiment Scores of Verb Phrases for Vietnamese—0
Concept-Based Embeddings for Natural Language Processing—0
Concreteness and Subjectivity as Dimensions of Lexical Meaning—0
Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation—0
Confidence Preservation Property in Knowledge Distillation Abstractions—0
Comparison of String Similarity Measures for Obscenity Filtering—0
Confirmatory Aspect-based Opinion Mining Processes—0
Confirming the Non-compositionality of Idioms for Sentiment Analysis—0
CONFLATOR: Incorporating Switching Point based Rotatory Positional Encodings for Code-Mixed Language Modeling—0
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis—0
Connecting Humanities and Social Sciences: Applying Language and Speech Technology to Online Panel Surveys—0
connotation_clashers at SemEval-2022 Task 6: The effect of sentiment analysis on sarcasm detection—0
Connotation in Translation—0
Connotation Lexicon: A Dash of Sentiment Beneath the Surface Meaning—0
ConnotationWordNet: Learning Connotation over the Word+Sense Network—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
Breaking NLP: Using Morphosyntax, Semantics, Pragmatics and World Knowledge to Fool Sentiment Analysis Systems—0
Aspect-Based Sentiment Analysis using BERT—0
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing—0
Construction of Emotional Lexicon Using Potts Model—0
Construction of Vietnamese SentiWordNet by using Vietnamese Dictionary—0
Comparison of Short-Text Sentiment Analysis Methods for Croatian—0
Comparison of Machine Learning for Sentiment Analysis in Detecting Anxiety Based on Social Media Data—0
Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization—0
Context-aware Sentiment Word Identification: sentiword2vec—0
A Multi-Dimensional Bayesian Approach to Lexical Style—0
Context-Enhanced Citation Sentiment Detection—0
AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis—0
Aspect-based Sentiment Analysis with Opinion Tree Generation—0
A Deep Convolutional Neural Network-based Model for Aspect and Polarity Classification in Hausa Movie Reviews—0
A Multidisciplinary Approach to Telegram Data Analysis—0
Comparing zero-shot self-explanations with human rationales in multilingual text classification—0
Contextual Augmented Global Contrast for Multimodal Intent Recognition—0
Comparing Multilingual Comparable Articles Based On Opinions—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—0
Aspect-based Sentiment Classification via Reinforcement Learning—0
Cross-Modality Gated Attention Fusion for Multimodal Sentiment Analysis—0
Aspect-based Opinion Summarization with Convolutional Neural Networks—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
Contextual Recurrent Units for Cloze-style Reading Comprehension—0
Contextual Sentence Analysis for the Sentiment Prediction on Financial Data—0
Contextual Text Embeddings for Twi—0
Comparing methods for Twitter 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