Prediction of Mental Health Problems among Higher Education Student Using Machine Learning

Shafiee, Nor Safika Mohd; Mutalib, Sofianita

doi:10.5815/ijeme.2020.06.01

Cited by 24 publications

(21 citation statements)

References 30 publications

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“…The lexicon-based technique generates flawless dictionaries, whereas the machine learning technique concentrates on feature vectors. The general workflow of sentiment analysis consists of a few processes: goal setting, text preprocessing, parsing the content, text refinement, and analysis and scoring [14].…”

Section: Methodsmentioning

confidence: 99%

Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis

Rahim¹,

Abdul-Rahman²,

Mahmud³

2021

IJACSA

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Mobile telecommunication companies in Malaysia have been widely used in the recent decade. There is intense competition among them to keep and gain new customers by offering various services. The reviews of the services by the customers are commonly shared on social media such as Twitter. Those reviews are essential for mobile telecommunication companies to improve their services and at the same time to keep their customers from churning to another company. Hence, this study focuses on the public sentiment on Twitter towards mobile telecommunication services in Malaysia. Data on Twitter was scraped using three keywords: Celcom, Digi, and Maxis. The keywords used to refer to Malaysia's top three mobile telecommunication companies. The timeline for the tweets was between December 2020 until January 2021 and was based on the promotion sales commonly used by the organisation to boost their sales which is called Year End Sales. Corpus-based approach and Machine Learning model using RapidMiner were used in this study, namely, Support Vector Machine (SVM), Naïve Bayes, and Deep Learning. The corpus determines the sentiment from the tweets, either positive, negative, or neutral. The models' performances were compared in terms of accuracy, and the outcome shows that Deep Learning classifiers have the highest performance compared to other classifiers. The results of this sentiment analysis are visualised for easy understanding.

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Section: Methodsmentioning

confidence: 99%

Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis

Rahim¹,

Abdul-Rahman²,

Mahmud³

2021

IJACSA

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“…In this part, fundamental basis of the methodology was quite appropriate for implementation of formulated research goal. In addition, in the process of research, we have revealed how these attitudes affect the emotional wellbeing of pupils and the state of their mental health [16].…”

Section: Methodsmentioning

confidence: 99%

Justice in Pedagogical Interactions as a Factor of Mental Health of Participants in Educational Process of Participants in Educational Process

Klochek¹,

Telychko²

2021

Proceedings of the International Conference on Economics, Law and Education Research (ELER 2021)

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The article under consideration is devoted to psychological and pedagogical study of the factor of mental health of participants of educational process, precisely, the establishment of pedagogical interaction between them on the basis of equity and objectivity. Mental health has been defined as a state of psychological stability and emotional well-being of an individual, which had been caused by equity of social interactions and appreciative attitude of its participants to one another. It has been testified that mental health of pupils is established and improved in the process of pedagogical interaction due to fair, impartial, respectful attitude of a teacher towards them. It has been clarified, that justified actions of a teacher affect psycho-emotional state of pupils, increase the productivity of their activities and development of subjectivity. On the other hand, unjustified actions cause negative feelings and are accompanied with emotions of rejection, anger, indignation. It has been clarified that in the process of pedagogical interactions pupils are harmonized with a fair teacher, achieve social and psychological balance, emotional comfort, which has a positive effect on their mental health.

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“…Research showed that ML algorithms have proven extremely useful for addressing many predictions and classification problems with broad application scope, including customer classification and segmentation [5], market analysis [6], [7], and education [8]. Unfortunately, ML is relatively limited and very far from being used in real estate applications mainly in the Malaysian sense.…”

Section: Introductionmentioning

confidence: 99%

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

Abdul-Rahman¹,

Zulkifley²,

Ibrahim³

et al. 2021

IJACSA

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House price is affected significantly by several factors and determining a reasonable house price involves a calculative process. This paper proposes advanced machine learning (ML) approaches for house price prediction. Two recent advanced ML algorithms, namely LightGBM and XGBoost were compared with two traditional approaches: multiple regression analysis and ridge regression. This study utilizes a secondary dataset called 'Property Listing in Kuala Lumpur', gathered from Kaggle and Google Map, containing 21984 observations with 11 variables, including a target variable. The performance of the ML models was evaluated using mean absolute error (MAE), root mean square error (RMSE), and adjusted r-squared value. The findings revealed that the house price prediction model based on XGBoost showed the highest performance by generating the lowest MAE and RMSE, and the closest adjusted r-squared value to one, consistently outperformed other ML models. A new dataset which consists of 1300 samples was deployed at the model deployment stage. It was found that the percentage of the variance between the actual and predicted price was relatively small, which indicated that this model is reliable and acceptable. This study can greatly assist in predicting future house prices and the establishment of real estate policies.

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Prediction of Mental Health Problems among Higher Education Student Using Machine Learning

Cited by 24 publications

References 30 publications

Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis

Customers’ Opinions on Mobile Telecommunication Services in Malaysia using Sentiment Analysis

Justice in Pedagogical Interactions as a Factor of Mental Health of Participants in Educational Process of Participants in Educational Process

Advanced Machine Learning Algorithms for House Price Prediction: Case Study in Kuala Lumpur

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