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 4351–4400 of 5630 papers

TitleStatusHype
A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining—0
A Large-Scale Japanese Dataset for Aspect-based Sentiment Analysis—0
A Large Scale Speech Sentiment Corpus—0
A Large Wordnet-based Sentiment Lexicon for Polish—0
A Leveled Reading Corpus of Modern Standard Arabic—0
A Lexicon-Based Supervised Attention Model for Neural Sentiment Analysis—0
Alibaba at IJCNLP-2017 Task 2: A Boosted Deep System for Dimensional Sentiment Analysis of Chinese Phrases—0
A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets—0
Aligning context-based statistical models of language with brain activity during reading—0
Aligning Entity Names with Online Aliases on Twitter—0
Aligning Human and Machine Attention for Enhanced Supervised Learning—0
A Linguistically Informed Convolutional Neural Network—0
A Linked Data Model for Multimodal Sentiment and Emotion Analysis—0
A literature survey on student feedback assessment tools and their usage in sentiment analysis—0
All-in-One: A Deep Attentive Multi-task Learning Framework for Humour, Sarcasm, Offensive, Motivation, and Sentiment on Memes—0
ALVIN: Active Learning Via INterpolation—0
A Machine Learning Approach to Detect Customer Satisfaction From Multiple Tweet Parameters—0
A Machine Learning, Natural Language Processing Analysis of Youth Perspectives: Key Trends and Focus Areas for Sustainable Youth Development Policies—0
AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis—0
Amharic Negation Handling—0
AMI\&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis—0
A Mixed-Methods Analysis of Western and Hong Kong–based Reporting on the 2019–2020 Protests—0
AMOA: Global Acoustic Feature Enhanced Modal-Order-Aware Network for Multimodal Sentiment Analysis—0
Amobee at SemEval-2017 Task 4: Deep Learning System for Sentiment Detection on Twitter—0
Amobee at SemEval-2018 Task 1: GRU Neural Network with a CNN Attention Mechanism for Sentiment Classification—0
Amplifying Aspect-Sentence Awareness: A Novel Approach for Aspect-Based Sentiment Analysis—0
Amrita\_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets—0
A Multi-Agent Probabilistic Inference Framework Inspired by Kairanban-Style CoT System with IdoBata Conversation for Debiasing—0
A multiclass Q-NLP sentiment analysis experiment using DisCoCat—0
A Multi-Dimensional Bayesian Approach to Lexical Style—0
A Multidisciplinary Approach to Telegram Data Analysis—0
A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis—0
A Multi-lingual Annotated Dataset for Aspect-Oriented Opinion Mining—0
A Multilingual Sentiment Lexicon for Low-Resource Language Translation using Large Languages Models and Explainable AI—0
A Multimodal Framework for Topic Propagation Classification in Social Networks—0
A Multimodal Sentiment Dataset for Video Recommendation—0
A Multiplicative Model for Learning Distributed Text-Based Attribute Representations—0
A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification—0
A Multi-Source Entity-Level Sentiment Corpus for the Financial Domain: The FinLin Corpus—0
A Multi-task Approach to Predict Likability of Books—0
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction—0
A multi-task learning network using shared BERT models for aspect-based sentiment analysis—0
A Multi-Task Text Classification Pipeline with Natural Language Explanations: A User-Centric Evaluation in Sentiment Analysis and Offensive Language Identification in Greek Tweets—0
A Multi- versus a Single-classifier Approach for the Identification of Modality in the Portuguese Language—0
A Multi-View Sentiment Corpus—0
An Account of Opinion Implicatures—0
An Advanced Press Review System Combining Deep News Analysis and Machine Learning Algorithms—0
An Algorithm for Routing Capsules in All Domains—0
An Algorithm for Routing Vectors in Sequences—0
Analyse de sentiments \`a base d'aspects par combinaison de r\'eseaux profonds : application \`a des avis en fran (A combination of deep learning methods for aspect-based sentiment analysis : application to French reviews)—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