Health-Related Tweets Classification: A Survey

Kothuru, Srinivasulu

doi:10.1007/978-981-15-7234-0_22

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…At the end of 2018, Google has built one such model, named BERT, that outperforms nearly all existing deep learning models in several NLP tasks [23][24][25]. BERT has recently obtained state-of-the-art results for a wide variety of NLP tasks, such as extracting clinical information for breast cancer [26] and analysis for biomedical clinical data [27].…”

Section: Deep Learning In the Medical Domainmentioning

confidence: 99%

Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study

Bitar

Babour

Nafa

et al. 2022

IJERPH

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Despite the availability of online educational resources about human papillomavirus (HPV), many women around the world may be prevented from obtaining the necessary knowledge about HPV. One way to mitigate the lack of HPV knowledge is the use of auto-generated text summarization tools. This study compares the level of HPV knowledge between women who read an auto-generated summary of HPV made using the BERT deep learning model and women who read a long-form text of HPV. We randomly assigned 386 women to two conditions: half read an auto-generated summary text about HPV (n = 193) and half read an original text about HPV (n = 193). We administrated measures of HPV knowledge that consisted of 29 questions. As a result, women who read the original text were more likely to correctly answer two questions on the general HPV knowledge subscale than women who read the summarized text. For the HPV testing knowledge subscale, there was a statistically significant difference in favor of women who read the original text for only one question. The final subscale, HPV vaccination knowledge questions, did not significantly differ across groups. Using BERT for text summarization has shown promising effectiveness in increasing women’s knowledge and awareness about HPV while saving their time.

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Section: Deep Learning In the Medical Domainmentioning

confidence: 99%

Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study

Bitar

Babour

Nafa

et al. 2022

IJERPH

View full text Add to dashboard Cite

show abstract

Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021

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This paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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Health-Related Tweets Classification: A Survey

Cited by 2 publications

References 24 publications

Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study

Increasing Women’s Knowledge about HPV Using BERT Text Summarization: An Online Randomized Study

Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

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