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 3301–3350 of 5630 papers

TitleStatusHype
Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction—0
Story Ending Prediction by Transferable BERTCode0
Adaptation of Deep Bidirectional Multilingual Transformers for Russian LanguageCode0
ERNIE: Enhanced Language Representation with Informative EntitiesCode0
System Demo for Transfer Learning from Vision to Language using Domain Specific CNN Accelerator for On-Device NLP Applications—0
Machine Learning based English Sentiment Analysis—0
Gated Convolutional Neural Networks for Domain Adaptation—0
Controlled CNN-based Sequence Labeling for Aspect Extraction—0
Multi-task Learning for Multi-modal Emotion Recognition and Sentiment Analysis—0
A Comparison of Techniques for Sentiment Classification of Film Reviews—0
Targeted Sentiment Analysis: A Data-Driven CategorizationCode0
Where does active travel fit within local community narratives of mobility space and place?—0
Models in the Wild: On Corruption Robustness of NLP Systems—0
Language-Agnostic Model for Aspect-Based Sentiment Analysis—0
A system for the 2019 Sentiment, Emotion and Cognitive State Task of DARPAs LORELEI project—0
Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying—0
Sentiment Classification using N-gram IDF and Automated Machine Learning—0
Evaluating Recurrent Neural Network Explanations—0
Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site—0
PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis—0
Empirical Evaluation of Leveraging Named Entities for Arabic Sentiment Analysis—0
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages—0
Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding—0
Zero-Shot Cross-Lingual Opinion Target Extraction—0
BowTie - A deep learning feedforward neural network for sentiment analysis—0
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment AnalysisCode0
Cross-Lingual Sentiment QuantificationCode0
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment ClassificationCode0
Deep-Sentiment: Sentiment Analysis Using Ensemble of CNN and Bi-LSTM Models—0
LICD: A Language-Independent Approach for Aspect Category Detection—0
Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records—0
Combining Sentiment Lexica with a Multi-View Variational AutoencoderCode0
Deep Learning Sentiment Analysis of Amazon.com Reviews and Ratings—0
Advancing NLP with Cognitive Language Processing SignalsCode0
Twitter Sentiment Analysis using Distributed Word and Sentence Representation—0
Sentiment analysis with genetically evolved Gaussian kernels—0
Imbalanced Sentiment Classification Enhanced with Discourse Marker—0
Distilling Task-Specific Knowledge from BERT into Simple Neural NetworksCode0
Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification—0
Micro-expression detection in long videos using optical flow and recurrent neural networks—0
Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary SentenceCode0
An end-to-end Neural Network Framework for Text Clustering—0
Affect in Tweets Using Experts Model—0
Cloze-driven Pretraining of Self-attention Networks—0
Effects of padding on LSTMs and CNNs—0
Sentiment Analysis on IMDB Movie Comments and Twitter Data by Machine Learning and Vector Space Techniques—0
Training Neural Networks for Aspect Extraction Using Descriptive Keywords Only—0
Market Trend Prediction using Sentiment Analysis: Lessons Learned and Paths ForwardCode0
Offensive Language Analysis using Deep Learning ArchitectureCode0
Twitter Speaks: A Case of National Disaster Situational Awareness—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