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 3151–3200 of 5630 papers

TitleStatusHype
Sentiment Polarity Detection in Azerbaijani Social News Articles—0
SenZi: A Sentiment Analysis Lexicon for the Latinised Arabic (Arabizi)—0
Transfer Learning Between Related Tasks Using Expected Label ProportionsCode0
Learning with Noisy Labels for Sentence-level Sentiment Classification—0
Can Sentiment Analysis Reveal Structure in a Plotless Novel?—0
NEZHA: Neural Contextualized Representation for Chinese Language UnderstandingCode0
Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions—0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
Cross-domain Aspect Category Transfer and Detection via Traceable Heterogeneous Graph Representation LearningCode0
Single Training Dimension Selection for Word Embedding with PCA—0
Sequential Learning of Convolutional Features for Effective Text Classification—0
A Joint Model for Aspect-Category Sentiment Analysis with Shared Sentiment Prediction Layer—0
FinBERT: Financial Sentiment Analysis with Pre-trained Language ModelsCode0
Non-local Recurrent Neural Memory for Supervised Sequence Modeling—0
Rethinking Attribute Representation and Injection for Sentiment ClassificationCode0
uniblock: Scoring and Filtering Corpus with Unicode Block InformationCode0
Domain-Invariant Feature Distillation for Cross-Domain Sentiment Classification—0
Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics—0
When Low Resource NLP Meets Unsupervised Language Model: Meta-pretraining Then Meta-learning for Few-shot Text ClassificationCode0
Fine-grained Sentiment Analysis with Faithful Attention—0
TDAM: a Topic-Dependent Attention Model for Sentiment Analysis—0
Shallow Domain Adaptive Embeddings for Sentiment Analysis—0
Integrating Multimodal Information in Large Pretrained TransformersCode0
Aspect and Opinion Terms Extraction Using Double Embeddings and Attention Mechanism for Indonesian Hotel Reviews—0
A Deep Evolutionary Approach to Bioinspired Classifier Optimisation for Brain-Machine Interaction—0
Variational Fusion for Multimodal Sentiment Analysis—0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding—0
A Simple and Effective Approach for Fine Tuning Pre-trained Word Embeddings for Improved Text ClassificationCode0
Sentiment Analysis of Typhoon Related Tweets using Standard and Bidirectional Recurrent Neural Networks—0
Data Set for Stance and Sentiment Analysis from User Comments on Croatian News—0
Lexicon Guided Attentive Neural Network Model for Argument Mining—0
Mazajak: An Online Arabic Sentiment Analyser—0
Entity-level Classification of Adverse Drug Reactions: a Comparison of Neural Network Models—0
Equity Beyond Bias in Language Technologies for Education—0
Improving Sentiment Classification in Slovak LanguageCode0
Sentiment Analysis at SEPLN (TASS)-2019: Sentiment Analysis at Tweet level using Deep Learning—0
Improving classification of Adverse Drug Reactions through Using Sentiment Analysis and Transfer Learning—0
Sentiment Analysis for Multilingual CorporaCode0
Adversarial Attack on Sentiment Classification—0
hULMonA: The Universal Language Model in ArabicCode0
Sentiment Analysis Model for Opinionated Awngi Text: Case of Music Reviews—0
Confirming the Non-compositionality of Idioms for Sentiment Analysis—0
Syntax-Ignorant N-gram Embeddings for Sentiment Analysis of Arabic Dialects—0
Confirmatory Aspect-based Opinion Mining Processes—0
Airbnb Price Prediction Using Machine Learning and Sentiment AnalysisCode0
Learning Invariant Representations for Sentiment Analysis: The Missing Material is Datasets—0
Fusing location and text features for sentiment classification—0
SpanBERT: Improving Pre-training by Representing and Predicting SpansCode0
Interpretable and Steerable Sequence Learning via PrototypesCode0
SentiMATE: Learning to play Chess through Natural Language Processing—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