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

Showing 26512700 of 5630 papers

TitleStatusHype
Morphological Skip-Gram: Using morphological knowledge to improve word representation0
Mono vs Multilingual Transformer-based Models: a Comparison across Several Language TasksCode0
A novel approach to sentiment analysis in Persian using discourse and external semantic information0
Feature-level Rating System using Customer Reviews and Review Votes0
Hierarchical Interaction Networks with Rethinking Mechanism for Document-level Sentiment AnalysisCode1
Towards Debiasing Sentence RepresentationsCode1
A Framework for Capturing and Analyzing Unstructured and Semi-structured Data for a Knowledge Management System0
What Can We Learn From Almost a Decade of Food TweetsCode0
Cautious Monotonicity in Case-Based Reasoning with Abstract Argumentation0
Attack of the Tails: Yes, You Really Can Backdoor Federated LearningCode1
Advances of Transformer-Based Models for News Headline GenerationCode1
Automatic Detection of Sexist Statements Commonly Used at the WorkplaceCode0
Research on Annotation Rules and Recognition Algorithm Based on Phrase Window0
The impact of political party/candidate on the election results from a sentiment analysis perspective using #AnambraDecides2017 tweets0
EmotionGIF-Yankee: A Sentiment Classifier with Robust Model Based Ensemble MethodsCode0
Tweets Sentiment Analysis via Word Embeddings and Machine Learning Techniques0
Exploratory Analysis of COVID-19 Related Tweets in North America to Inform Public Health Institutes0
News Sentiment Analysis0
Sentiment Analysis on Customer Responses0
Sentiment Analysis on Social Media Content0
A Novel BGCapsule Network for Text Classification0
Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey0
Sequential Domain Adaptation through Elastic Weight Consolidation for Sentiment Analysis0
Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic0
Applying Transformers and Aspect-based Sentiment Analysis approaches on Sarcasm Detection0
Multilogue-Net: A Context-Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in ConversationCode1
e-Commerce and Sentiment Analysis: Predicting Outcomes of Class Action Lawsuits0
Recognizing Euphemisms and Dysphemisms Using Sentiment Analysis0
Detecting Sarcasm in Conversation Context Using Transformer-Based Models0
Corpus based Amharic sentiment lexicon generation0
Metaphor Detection Using Contextual Word Embeddings From Transformers0
Negation handling for Amharic sentiment classification0
SentiTel: TABSA for Twitter reviews on Uganda Telecoms0
Towards Reversal-Based Textual Data Augmentation for NLI Problems with Opposable Classes0
Evaluating and Enhancing the Robustness of Neural Network-based Dependency Parsing Models with Adversarial Examples0
SpanMlt: A Span-based Multi-Task Learning Framework for Pair-wise Aspect and Opinion Terms Extraction0
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment AnalysisCode1
Dependency Graph Enhanced Dual-transformer Structure for Aspect-based Sentiment Classification0
Adversarial and Domain-Aware BERT for Cross-Domain Sentiment Analysis0
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks0
Cross-Lingual Unsupervised Sentiment Classification with Multi-View Transfer Learning0
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair ExtractionCode1
Aspect Sentiment Classification with Document-level Sentiment Preference Modeling0
GLUECoS: An Evaluation Benchmark for Code-Switched NLP0
Modelling Context and Syntactical Features for Aspect-based Sentiment AnalysisCode1
Sentiment and Emotion help Sarcasm? A Multi-task Learning Framework for Multi-Modal Sarcasm, Sentiment and Emotion Analysis0
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of ModalityCode1
Weight Poisoning Attacks on Pretrained Models0
Feature Projection for Improved Text Classification0
A Data-driven Neural Network Architecture for Sentiment Analysis0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Word+ES (Scratch)Attack Success Rate100Unverified
2MT-DNN-SMARTAccuracy97.5Unverified
3T5-11BAccuracy97.5Unverified
4MUPPET Roberta LargeAccuracy97.4Unverified
5T5-3BAccuracy97.4Unverified
6ALBERTAccuracy97.1Unverified
7StructBERTRoBERTa ensembleAccuracy97.1Unverified
8XLNet (single model)Accuracy97Unverified
9SMARTRoBERTaDev Accuracy96.9Unverified
10ELECTRAAccuracy96.9Unverified
#ModelMetricClaimedVerifiedStatus
1RoBERTa-large with LlamBERTAccuracy96.68Unverified
2RoBERTa-largeAccuracy96.54Unverified
3XLNetAccuracy96.21Unverified
4Heinsen Routing + RoBERTa LargeAccuracy96.2Unverified
5RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy96.1Unverified
6GraphStarAccuracy96Unverified
7DV-ngrams-cosine with NB sub-sampling + RoBERTa.baseAccuracy95.94Unverified
8DV-ngrams-cosine + RoBERTa.baseAccuracy95.92Unverified
9Roberta_Large ST + Cosine Similarity LossAccuracy95.9Unverified
10BERT large finetune UDAAccuracy95.8Unverified
#ModelMetricClaimedVerifiedStatus
1Llama-3.3-70B + CAPOAccuracy62.27Unverified
2Mistral-Small-24B + CAPOAccuracy 60.2Unverified
3Heinsen Routing + RoBERTa LargeAccuracy59.8Unverified
4RoBERTa-large+Self-ExplainingAccuracy59.1Unverified
5Qwen2.5-32B + CAPOAccuracy 59.07Unverified
6Heinsen Routing + GPT-2Accuracy58.5Unverified
7BCN+Suffix BiLSTM-Tied+CoVeAccuracy56.2Unverified
8BERT LargeAccuracy55.5Unverified
9LM-CPPF RoBERTa-baseAccuracy54.9Unverified
10BCN+ELMoAccuracy54.7Unverified
#ModelMetricClaimedVerifiedStatus
1Char-level CNNError4.88Unverified
2SVDCNNError4.74Unverified
3LEAMError4.69Unverified
4fastText, h=10, bigramError4.3Unverified
5SWEM-hierError4.19Unverified
6SRNNError3.96Unverified
7M-ACNNError3.89Unverified
8DNC+CUWError3.6Unverified
9CCCapsNetError3.52Unverified
10Block-sparse LSTMError3.27Unverified
#ModelMetricClaimedVerifiedStatus
1Millions of EmojiTraining Time1,500Unverified
2VLAWEAccuracy93.3Unverified
3RoBERTa-large 355M + Entailment as Few-shot LearnerAccuracy92.5Unverified
4AnglE-LLaMA-7BAccuracy91.09Unverified
5byte mLSTM7Accuracy86.8Unverified
6MEANAccuracy84.5Unverified
7RNN-CapsuleAccuracy83.8Unverified
8Capsule-BAccuracy82.3Unverified
9SuBiLSTM-TiedAccuracy81.6Unverified
10USE_T+CNNAccuracy81.59Unverified