“I Let Depression and Anxiety Drown Me…”: Identifying Factors Associated With Resilience Based on Journaling Using Machine Learning and Thematic Analysis

Oduntan, Adenrele; Oyebode, Oladapo; Beltran, Amelia Hernandez; Fowles, Jonathon R.; Steeves, Darren; Orji, Rita

doi:10.1109/jbhi.2022.3149862

Cited by 11 publications

(1 citation statement)

References 78 publications

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“…Previous studies have explored the approaches and challenges of sentiment analysis based on various machine learning [16], [19], [21] and its implementation across board research areas: in the tourism context [11], in the medical field for coronary angiography [22], and geography are related to shallow landslide susceptibility mapping [23]. The data input for the machine learning process could be derived from various sources: Twitter data [24]- [31], journaling entries [32], movie dataset [33], [34], reviews on various applications: Traveling application [35] and Shopping application [36], [37], and user's review on Google Play Store [21], [38]- [40] and App Store [20]. Machine learning approaches are performed to categorize the polarity of sentiment based on a train and test dataset.…”

Section: Introductionmentioning

confidence: 99%

Assessing public satisfaction of public service application using supervised machine learning

Zharif Mustaqim,

Melani Puspasari,

Tri Utami

et al. 2024

IJ-AI

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The COVID-19 pandemic has enormously affected the economic situation worldwide, including in Indonesia resulting in 30 million Indonesian tumbling into penury. The Ministry of Social Affairs initiated a program to distribute social assistance aimed at the poorest households. ‘Aplikasi Cek Bansos’ is a public service application that aims to validate their status towards the social assistance program. Understanding the public sentiment and factors affecting public satisfaction levels is crucial to be performed. The goal of this study is to perform a comparative study of supervised machine learning to learn the sentiment of the public and the dominant variable resulting in public satisfaction. Support vector machine, Naïve Bayes dan K-nearest neighbor (KNN) are performed to seek the highest accuracy. This experiment discovered that the KNN algorithm produced outstanding performance where the accuracy hit 99.21%. Sentiment prediction indicated negative perception as the majority covering 83.81%. Trigrams analysis is performed to learn themes affecting satisfaction levels toward the application. Negative themes are grouped into the following categories: App instability, hope for improvement, navigation issues, and low-quality content. Some recommendations are offered for the Ministry of Social Affairs and developers, to overcome negative feedback and enhance public satisfaction level towards the application.

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