ArticleJournal of healthcare engineering2022
A Fine-Tuned BERT-Based Transfer Learning Approach for Text Classification.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed.
- A deep learning framework for emotion recognition in music using multimodal data fusion.Scientific reports · 2026Article
- IBNN: an integrated BERT-neural network framework for sentiment and emotion based text analysis.Scientific reports · 2026Article
- Ensemble transformer with post-hoc explanations for depression emotion and severity detection.iScience · 2026Article
- Article
- Explainable AI-Driven Analysis of Radiology Reports Using Text and Image Data: Experimental Study.JMIR formative research · 2025Article
- SmellyCode++: Multi-Label Dataset for Code Smell Detection.Scientific data · 2025Article
- Towards Identifying Objectivity in Short Informal Text.Entropy (Basel, Switzerland) · 2025Article
- Implementing large language model and retrieval augmented generation to extract geographic locations of illicit transnational kidney trade.International journal of health geographics · 2025Article
- BERT ensemble based MBR framework for android malware detection.Scientific reports · 2025Article
- Transformer-based heart language model with electrocardiogram annotations.Scientific reports · 2025Article
- Identifying Misinformation About Unproven Cancer Treatments on Social Media Using User-Friendly Linguistic Characteristics: Content Analysis.JMIR infodemiology · 2025Article
- Leveraging deep learning to detect stance in Spanish tweets on COVID-19 vaccination.JAMIA open · 2025Article
- Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism.Scientific reports · 2025Article
- Integrated ensemble of BERT- and feature-based models for authorship attribution in Japanese literary works.Frontiers in artificial intelligence · 2025Article
- From theDepression and anxiety · 2025Article
- Responsible Detection and Mitigation of AI-Generated Text Using Hybrid Neural Networks and Feature Fusion: Toward Trustworthy Content Management in the Era of Large Language Models.International journal of computational intelligence systems · 2025Article
- Learning self-supervised molecular representations for drug-drug interaction prediction.BMC bioinformatics · 2024Article
- Comparative analysis of BERT-based and generative large language models for detecting suicidal ideation: a performance evaluation study.Cadernos de saude publica · 2024Article
- Leveraging textual information for social media news categorization and sentiment analysis.PloS one · 2024Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Text Classification problem has been thoroughly studied in information retrieval problems and data mining tasks. It is beneficial in multiple tasks including medical diagnose health and care department, targeted marketing, entertainment industry, and group filtering processes. A recent innovation in both data mining and natural language processing gained the attention of researchers from all over the world to develop automated systems for text classification. NLP allows categorizing documents containing different texts. A huge amount of data is generated on social media sites through social media users. Three datasets have been used for experimental purposes including the COVID-19 fake news dataset, COVID-19 English tweet dataset, and extremist-non-extremist dataset which contain news blogs, posts, and tweets related to coronavirus and hate speech. Transfer learning approaches do not experiment on COVID-19 fake news and extremist-non-extremist datasets. Therefore, the proposed work applied transfer learning classification models on both these datasets to check the performance of transfer learning models. Models are trained and evaluated on the accuracy, precision, recall, and F1-score. Heat maps are also generated for every model. In the end, future directions are proposed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.