ReviewComputational intelligence and neuroscience2022
A Complete Process of Text Classification System Using State-of-the-Art NLP Models.
Review in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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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.
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Who cites it
16 citing papers in PubMed.
- Toward Automating the Selection of Articles Reporting EQ-5D Data for Systematic Literature Reviews Using Large Language Models: Algorithm Development and Evaluation Study.JMIR formative research · 2026Article
- A deep learning framework for emotion recognition in music using multimodal data fusion.Scientific reports · 2026Article
- A high-precision catalogue of landslide events in China based on news text mining with large language model.Scientific data · 2026Article
- Automated detection of corruption reports in text via deep reinforcement learning.Scientific reports · 2025Article
- Natural Language Processing framework for identifying abdominal aortic aneurysm repairs using unstructured electronic health records.Scientific reports · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Multi task opinion enhanced hybrid BERT model for mental health analysis.Scientific reports · 2025Article
- Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism.Scientific reports · 2025Article
- O2 supplementation disambiguation in clinical narratives to support retrospective COVID-19 studies.BMC medical informatics and decision making · 2024Article
- MLR-predictor: a versatile and efficient computational framework for multi-label requirements classification.Frontiers in artificial intelligence · 2024Article
- Contextualizing injury severity from occupational accident reports using an optimized deep learning prediction model.PeerJ. Computer science · 2024Article
- Large Language Models in Neurology Research and Future Practice.Neurology · 2023Article
- Challenges and Limitation Analysis of an IoT-Dependent System for Deployment in Smart Healthcare Using Communication Standards Features.Sensors (Basel, Switzerland) · 2023Article
- Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts.Computational intelligence and neuroscience · 2023Article
- A model of integrating convolution and BiGRU dual-channel mechanism for Chinese medical text classifications.PloS one · 2023Article
- An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid advancement of information technology, online information has been exponentially growing day by day, especially in the form of text documents such as news events, company reports, reviews on products, stocks-related reports, medical reports, tweets, and so on. Due to this, online monitoring and text mining has become a prominent task. During the past decade, significant efforts have been made on mining text documents using machine and deep learning models such as supervised, semisupervised, and unsupervised. Our area of the discussion covers state-of-the-art learning models for text mining or solving various challenging NLP (natural language processing) problems using the classification of texts. This paper summarizes several machine learning and deep learning algorithms used in text classification with their advantages and shortcomings. This paper would also help the readers understand various subtasks, along with old and recent literature, required during the process of text classification. We believe that readers would be able to find scope for further improvements in the area of text classification or to propose new techniques of text classification applicable in any domain of their interest.
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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.