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 1951–2000 of 5630 papers

TitleStatusHype
When does CLIP generalize better than unimodal models? When judging human-centric concepts—0
Sentiment Analysis on Code-Switched Dravidian Languages with Kernel Based Extreme Learning Machines—0
ETMS@IITKGP at SemEval-2022 Task 10: Structured Sentiment Analysis Using A Generative ApproachCode0
SentEMO: A Multilingual Adaptive Platform for Aspect-based Sentiment and Emotion Analysis—0
SURREY-CTS-NLP at WASSA2022: An Experiment of Discourse and Sentiment Analysis for the Prediction of Empathy, Distress and Emotion—0
Scoping natural language processing in Indonesian and Malay for education applications—0
Discrete Opinion Tree Induction for Aspect-based Sentiment AnalysisCode0
Adversarial Soft Prompt Tuning for Cross-Domain Sentiment Analysis—0
Detection of Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density EstimationCode0
Aspect-based Sentiment Analysis with Opinion Tree Generation—0
Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining—0
Findings of the Shared Task on Multimodal Sentiment Analysis and Troll Meme Classification in Dravidian Languages—0
Are you a hero or a villain? A semantic role labelling approach for detecting harmful memes.—0
Seq2Path: Generating Sentiment Tuples as Paths of a Tree—0
Multimodal fusion via cortical network inspired losses—0
Learn from Structural Scope: Improving Aspect-Level Sentiment Analysis with Hybrid Graph Convolutional NetworksCode0
LyS_ACoruña at SemEval-2022 Task 10: Repurposing Off-the-Shelf Tools for Sentiment Analysis as Semantic Dependency Parsing—0
Sentiment Analysis of Cybersecurity Content on Twitter and Reddit—0
Do Transformer Models Show Similar Attention Patterns to Task-Specific Human Gaze?Code0
Twitter-Based Gender Recognition Using TransformersCode0
WaBERT: A Low-resource End-to-end Model for Spoken Language Understanding and Speech-to-BERT Alignment—0
A Vocabulary-Free Multilingual Neural Tokenizer for End-to-End Task Learning—0
Locally Aggregated Feature Attribution on Natural Language Model Understanding—0
Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification—0
You Are What You Write: Preserving Privacy in the Era of Large Language Models—0
yosm: A new yoruba sentiment corpus for movie reviewsCode0
Building Odia Shallow ParserCode0
Social Media Sentiment Analysis for Cryptocurrency Market Prediction—0
Mono vs Multilingual BERT for Hate Speech Detection and Text Classification: A Case Study in Marathi—0
Training Entire-Space Models for Target-oriented Opinion Words ExtractionCode0
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference—0
Challenges for Open-domain Targeted Sentiment Analysis—0
Sentiment Analysis of Political Tweets for Israel using Machine Learning—0
Survey of Aspect-based Sentiment Analysis DatasetsCode0
Assessment of Massively Multilingual Sentiment Classifiers—0
Mapping the Multilingual Margins: Intersectional Biases of Sentiment Analysis Systems in English, Spanish, and Arabic—0
Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets—0
Twitter Dataset on the Russo-Ukrainian War—0
Product Market Demand Analysis Using NLP in Banglish Text with Sentiment Analysis and Named Entity Recognition—0
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment AnalysisCode0
A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations—0
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis—0
Forecasting with Economic News—0
Example-based Hypernetworks for Out-of-Distribution GeneralizationCode0
A Survey on Aspect-Based Sentiment Classification—0
A Framework for Fast Polarity Labelling of Massive Data StreamsCode0
Feature Distribution Matching for Federated Domain GeneralizationCode0
Multi-channel CNN to classify nepali covid-19 related tweets using hybrid features—0
Fiber Bundle Morphisms as a Framework for Modeling Many-to-Many Maps—0
Combining dynamic local context focus and dependency cluster attention for aspect-level sentiment classificationCode0
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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