Social Media News Classification in Healthcare Communication

Majeed, Fiaz; Asif, Muhammad; Hassan, Muhammad; Abbas, Syed Ali; Lali, M. Ikramullah

doi:10.1166/jmihi.2019.2735

Cited by 7 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From a broader perspective, automated text classification has become a mainstream research area because of the numerous public digital text sources available. It has a wide range of applications including opinion mining, 11 event detection, 12 sentiment analysis, 13 question answering, 14 spam detection, 15 news categorization, 16 and so on. Nonetheless, the majority of text classification methods are well‐founded for long texts.…”

Section: Introductionmentioning

confidence: 99%

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Allegue

Abdellatif

Abed

2021

Concurrency and Computation

View full text Add to dashboard Cite

New business models underpinned by new standards of openness, flexibility, and agility are arising in the banking sector. Therefore, banks have to establish new strategies to keep and to extend their client base, especially with the explosive growth of customers' data and interaction touch-points. Hence, banks and fintechs are in fierce competition to transform customers' data that incorporate their transactions into pertinent and significant knowledge for decision making. In this article, we present a novel system aimed toward solving a long-standing and challenging issue: obtaining classifiers to automatically categorize bank transactions for a Smart Budget Manager. We fit and test our system using real data. The strength of our system lies in the novel combination of incremental machine learning algorithms with a natural language understanding for a fine-grained categorization of bank transactions. Our system serves as a base layer for other advanced banking applications such as segmentation, next best offers and credit scoring. Our system is deployed in a real banking application of a Tunisian bank and has shown bank and customer's satisfaction.

show abstract

Section: Introductionmentioning

confidence: 99%

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Allegue

Abdellatif

Abed

2021

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…The general methodology regarding how machine learning can help social media health news analysis is proposed [18]. A methodology for collecting, modeling, and displaying health-related patterns across 13 Twitter news sources is proposed.…”

Section: Related Workmentioning

confidence: 99%

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

2023

View full text Add to dashboard Cite

The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. The data are stratified into train and test sets to ensure equal representation of each category. Word embeddings are generated using transformer models, with the last hidden layer selected as the embedding. Mean pooling calculates a single vector representation called sentence embedding, capturing the overall meaning of the news article. The performance of Softmax and Random Forest, as well as the soft voting of both, is evaluated using evaluation measures such as accuracy, F1 score, precision, and recall. The study also contributes by evaluating the performance of Softmax and Random Forest individually. The macro-average F1 score is calculated to compare the performance of different transformer embeddings in the same experimental settings. The experiments reveal that MPNet versions v1 and v3 achieve the highest F1 score of 97.7% when combined with Random Forest, while T5 Large embedding achieves the highest F1 score of 98.2% when used with Softmax regression. MPNet v1 performs exceptionally well when used in the voting classifier, obtaining an impressive F1 score of 98.6%. In conclusion, the experiments validate the superiority of certain transformer models, such as MPNet v1, MPNet v3, and DistilRoBERTa, when used to calculate sentence embeddings within the Random Forest framework. The results also highlight the promising performance of T5 Large and RoBERTa Large in voting of Softmax regression and Random Forest. The voting classifier, employing transformer embeddings and ensemble learning techniques, consistently outperforms other baselines and individual algorithms. These findings emphasize the effectiveness of the voting classifier with transformer embeddings in achieving accurate and reliable predictions for news category classification tasks.

show abstract

“…Detection of dementia (including Alzheimer's disease) [20][21][22] Detection of anxiety [23][24][25] Medicalchain-Blockchain and healthcare [26][27][28] Genetics and genomics [29][30][31] Virtual reality in healthcare [32][33][34] Social media in healthcare [35][36][37] Robotic surgery [38][39][40]…”

Section: Emergent Topics Recommended Readingsmentioning

confidence: 99%

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

2019

View full text Add to dashboard Cite

The concepts of the smart city and the Internet of Things (IoT) have been facilitating the rollout of medical devices and systems to capture valuable information of humanity. A lot of artificial intelligence techniques have been demonstrated to be effective in smart city applications like energy, transportation, retail and control. In recent decade, retardation of the adoption of data analytics algorithms and systems in healthcare has been decreasing, and there is tremendous growth in data analytics research on healthcare data. The results of analytics aim at improving people's quality of life as well as relieving the issue of medical shortages. In this special issue "Data Analytics in Smart Healthcare", thirteen (13) papers have been published as the representative examples of recent developments. Guest Editors also highlight some emergent topics and opening challenges in healthcare analytics which follow the visions of the movement of healthcare analytics research.

show abstract

Social Media News Classification in Healthcare Communication

Cited by 7 publications

References 0 publications

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

SBM: A Smart Budget Manager in banking using machine learning, NLP, and NLU

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

Data Analytics in Smart Healthcare: The Recent Developments and Beyond

Contact Info

Product

Resources

About