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Twitter Arabic Sentiment Analysis to Detect Depression Using Machine Learning

Computers, Materials & Continua 2022年第5期71卷 3463-3477页

作者： Dhiaa A.Musleh Taef A.Alkhales Reem A.Almakki shahad e.alnajim Shaden K.Almarshad Rana S.Alhasaniah Sumayh S.Aljameel Abdullah A.Almuqhim Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Depression has been a major global concern for a long time,with the disease affecting aspects of many people’s daily lives,such as their moods,eating habits,and social *** Arabic culture,there is a lack of awareness regarding the importance of facing and curing mental health ***,people all over the world,including Arab citizens,tend to express their feelings openly on social media,especially Twitter,as it is a platform designed to enable the expression of emotions through short texts,pictures,or *** are inclined to treat their Twitter accounts as diaries because the platform affords them *** published studies have detected the occurrence of depression among Twitter users on the basis of data on tweets posted in english,but research on Arabic tweets is *** aim of the present work was to develop a model for analyzing Arabic users’tweets and detecting depression among Arabic Twitter *** expand the diversity of user tweets,by adding a new label(“neutral”)so the dataset include three classes(“depressed”,“non-depressed”,“neutral”).The model was created using machine learning classifiers and natural language processing techniques,such as Support Vector Machine(SVM),Random Forest(RF),Logistic Regression(LR),K-nearest Neighbors(KNN),AdaBoost,and Naïve Bayes(NB).The results showed that the RF classifier outperformed the others,registering an accuracy of 82.39%.

关键词： Depression sentiment analysis twitter supervised learning machine learning

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Bitcoin Candlestick Prediction with Deep Neural Networks Based on Real Time Data

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Computers, Materials & Continua 2021年第9期68卷 3215-3233页

作者： Reem K.Alkhodhairi shahad R.Aljalhami Norah K.Rusayni Jowharah F.Alshobaili Amal A.Al-Shargabi Abdulatif Alabdulatif Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia

Currently,Bitcoin is the world’s most popular *** price of Bitcoin is extremely volatile,which can be described as high-benefit and *** minimize the risk involved,a means of more accurately predicting the Bitcoin price is *** of the existing studies of Bitcoin prediction are based on historical(i.e.,benchmark)data,without considering the real-time(i.e.,live)*** mitigate the issue of price volatility and achieve more precise outcomes,this study suggests using historical and real-time data to predict the Bitcoin candlestick—or open,high,low,and close(OHLC)—*** a better prediction model,the present study proposes time series-based deep learning *** particular,two deep learning algorithms were applied,namely,long short-term memory(LSTM)and gated recurrent unit(GRU).Using real-time data,the Bitcoin candlesticks were predicted for three intervals:the next 4 h,the next 12 h,and the next 24 *** results showed that the best-performing model was the LSTM-based model with the 4-h *** particular,this model achieved a stellar performance with a mean absolute percentage error(MAPe)of 0.63,a root mean square error(RMSe)of 0.0009,a mean square error(MSe)of 9e-07,a mean absolute error(MAe)of 0.0005,and an R-squared coefficient(R2)of *** these results,the proposed prediction model has demonstrated its efficiency over the models proposed in previous *** findings of this study have considerable implications in the business field,as the proposed model can assist investors and traders in precisely identifying Bitcoin sales and buying opportunities.

关键词： Bitcoin prediction long short term memory gated recurrent unit deep neural networks real-time data

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