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 901–950 of 5630 papers

TitleStatusHype
Benchmarking Twitter Sentiment Analysis Tools—0
Benchmark on Peer Review Toxic Detection: A Challenging Task with a New Dataset—0
Benchmarks and models for entity-oriented polarity detection—0
Analyzing Sentiment in Classical Chinese Poetry—0
Benefactive/Malefactive Event and Writer Attitude Annotation—0
BenLLMEval: A Comprehensive Evaluation into the Potentials and Pitfalls of Large Language Models on Bengali NLP—0
AdvCodec: Towards A Unified Framework for Adversarial Text Generation—0
BERT2DNN: BERT Distillation with Massive Unlabeled Data for Online E-Commerce Search—0
BERT4GCN: Using BERT Intermediate Layers to Augment GCN for Aspect-based Sentiment Classification—0
Analyzing Public Reactions, Perceptions, and Attitudes during the MPox Outbreak: Findings from Topic Modeling of Tweets—0
A Thorough Investigation into the Application of Deep CNN for Enhancing Natural Language Processing Capabilities—0
BERTaú: Itaú BERT for digital customer service—0
BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis—0
BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text—0
BERT-based Financial Sentiment Index and LSTM-based Stock Return Predictability—0
Annotating Opinions and Opinion Targets in Student Course Feedback—0
BERTCaps: BERT Capsule for Persian Multi-Domain Sentiment Analysis—0
BERT-Deep CNN: State-of-the-Art for Sentiment Analysis of COVID-19 Tweets—0
Analysis of Chinese Tourists in Japan by Text Mining of a Hotel Portal Site—0
BERT Fine-Tuning for Sentiment Analysis on Indonesian Mobile Apps Reviews—0
Annotating the Interaction between Focus and Modality: the case of exclusive particles—0
A functional linguistic perspective on evaluation—0
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT—0
BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights—0
BERT or FastText? A Comparative Analysis of Contextual as well as Non-Contextual Embeddings—0
Analyzing Political Parody in Social Media—0
A comprehensive cross-language framework for harmful content detection with the aid of sentiment analysis—0
BESSTIE: A Benchmark for Sentiment and Sarcasm Classification for Varieties of English—0
Best Practices in the Creation and Use of Emotion Lexicons—0
Can ChatGPT Reproduce Human-Generated Labels? A Study of Social Computing Tasks—0
Better Document-level Sentiment Analysis from RST Discourse Parsing—0
Better Handling Coreference Resolution in Aspect Level Sentiment Classification by Fine-Tuning Language Models—0
Athena: Efficient Block-Wise Post-Training Quantization for Large Language Models Using Second-Order Matrix Derivative Information—0
A Text-Image Pair Is not Enough: Language-Vision Relation Inference with Auxiliary Modality Translation—0
Analyzing Political Figures in Real-Time: Leveraging YouTube Metadata for Sentiment Analysis—0
Beyond Metrics: Evaluating LLMs' Effectiveness in Culturally Nuanced, Low-Resource Real-World Scenarios—0
Beyond Multiword Expressions: Processing Idioms and Metaphors—0
A Text-Centered Shared-Private Framework via Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Beyond Sentiment: Leveraging Topic Metrics for Political Stance Classification—0
Beyond Sentiment: The Manifold of Human Emotions—0
Beyond the Black Box: Interpretability of LLMs in Finance—0
ATESA-BÆRT: A Heterogeneous Ensemble Learning Model for Aspect-Based Sentiment Analysis—0
Agent-Based Simulations of Online Political Discussions: A Case Study on Elections in Germany—0
标签先验知识增强的方面类别情感分析方法研究(Aspect-Category based Sentiment Analysis Enhanced by Label Prior Knowledge)—0
A novel Bayesian estimation-based word embedding model for sentiment analysis—0
Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages—0
Eradicating Social Biases in Sentiment Analysis using Semantic Blinding and Semantic Propagation Graph Neural Networks—0
Bias in Emotion Recognition with ChatGPT—0
Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification—0
Advancing Sentiment Analysis in Tamil-English Code-Mixed Texts: Challenges and Transformer-Based Solutions—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