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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 5101–5150 of 5630 papers

TitleStatusHype
Contextual and Position-Aware Factorization Machines for Sentiment Classification—0
Contextual Augmented Global Contrast for Multimodal Intent Recognition—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—0
Contextual explanation rules for neural clinical classifiers—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
Contrasting the efficiency of stock price prediction models using various types of LSTM models aided with sentiment analysis—0
Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion and Sentiment Classification—0
Controlled CNN-based Sequence Labeling for Aspect Extraction—0
Conversational Recommendation System using NLP and Sentiment Analysis—0
Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction—0
Convolutional neural network compression for natural language processing—0
Convolutional Neural Networks for Sentiment Classification on Business Reviews—0
Convolutional Neural Networks for Sentiment Analysis in Persian Social Media—0
Convolutional Neural Networks for Sentiment Analysis on Weibo Data: A Natural Language Processing Approach—0
Convolutional Sentence Kernel from Word Embeddings for Short Text Categorization—0
Convolution over Hierarchical Syntactic and Lexical Graphs for Aspect Level Sentiment Analysis—0
Coooolll: A Deep Learning System for Twitter Sentiment Classification—0
Co-Regression for Cross-Language Review Rating Prediction—0
Co-regularization Based Semi-supervised Domain Adaptation—0
CorMulT: A Semi-supervised Modality Correlation-aware Multimodal Transformer for Sentiment Analysis—0
#Coronavirus or #Chinesevirus?!: Understanding the negative sentiment reflected in Tweets with racist hashtags across the development of COVID-19—0
Corpora Preparation and Stopword List Generation for Arabic data in Social Network—0
Corporate Bankruptcy Prediction with BERT Model—0
Corporate Bankruptcy Prediction with Domain-Adapted BERT—0
Corpus Based Amharic Sentiment Lexicon Generation—0
Corpus based Amharic sentiment lexicon generation—0
Corpus-based discovery of semantic intensity scales—0
Corpus Creation for Sentiment Analysis in Code-Mixed Tulu Text—0
Corpus Statistics in Text Classification of Online Data—0
Correlating Facts and Social Media Trends on Environmental Quantities Leveraging Commonsense Reasoning and Human Sentiments—0
Correlation-Based Method for Sentiment Classification—0
Correlation-Decoupled Knowledge Distillation for Multimodal Sentiment Analysis with Incomplete Modalities—0
Correlations and Flow of Information between The New York Times and Stock Markets—0
Co-Simmate: Quick Retrieving All Pairwise Co-Simrank Scores—0
Cost and Benefit of Using WordNet Senses for Sentiment Analysis—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Cost-effective Deployment of BERT Models in Serverless Environment—0
Cost-Sensitive BERT for Generalisable Sentence Classification on Imbalanced Data—0
Co-training for Semi-supervised Sentiment Classification Based on Dual-view Bags-of-words Representation—0
Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics—0
COVID-19Base: A knowledgebase to explore biomedical entities related to COVID-19—0
COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations—0
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining—0
Covid-19 Public Sentiment Analysis for Indian Tweets Classification—0
COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification—0
COVID-19 sentiment analysis via deep learning during the rise of novel cases—0
COVID-19 Twitter Sentiment Classification Using Hybrid Deep Learning Model Based on Grid Search Methodology—0
CPH: Sentiment analysis of Figurative Language on Twitter \#easypeasy \#not—0
Show:102550
← PrevPage 103 of 113Next →
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