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http://dx.doi.org/10.22937/IJCSNS.2022.22.9.44

Machine Learning Algorithm Accuracy for Code-Switching Analytics in Detecting Mood  

Latib, Latifah Abd (Faculty of Communication, Visual Art and Computing, Universiti Selangor)
Subramaniam, Hema (Department of Software Engineering, Faculty of Computer Science and Information Technology, Universiti Malaya)
Ramli, Siti Khadijah (Faculty of Communication, Visual Art and Computing, Universiti Selangor)
Ali, Affezah (School of Liberal Arts & Sciences, Taylor's University)
Yulia, Astri (Department of Language Education, Faculty of Education and Social Sciences, University Selangor)
Shahdan, Tengku Shahrom Tengku (School Of Education & Human Sciences, Albukhary International University)
Zulkefly, Nor Sheereen (Faculty of Medicine and Health Sciences, Universiti Putra Malaysia)
Publication Information
International Journal of Computer Science & Network Security / v.22, no.9, 2022 , pp. 334-342 More about this Journal
Abstract
Nowadays, as we can notice on social media, most users choose to use more than one language in their online postings. Thus, social media analytics needs reviewing as code-switching analytics instead of traditional analytics. This paper aims to present evidence comparable to the accuracy of code-switching analytics techniques in analysing the mood state of social media users. We conducted a systematic literature review (SLR) to study the social media analytics that examined the effectiveness of code-switching analytics techniques. One primary question and three sub-questions have been raised for this purpose. The study investigates the computational models used to detect and measures emotional well-being. The study primarily focuses on online postings text, including the extended text analysis, analysing and predicting using past experiences, and classifying the mood upon analysis. We used thirty-two (32) papers for our evidence synthesis and identified four main task classifications that can be used potentially in code-switching analytics. The tasks include determining analytics algorithms, classification techniques, mood classes, and analytics flow. Results showed that CNN-BiLSTM was the machine learning algorithm that affected code-switching analytics accuracy the most with 83.21%. In addition, the analytics accuracy when using the code-mixing emotion corpus could enhance by about 20% compared to when performing with one language. Our meta-analyses showed that code-mixing emotion corpus was effective in improving the mood analytics accuracy level. This SLR result has pointed to two apparent gaps in the research field: i) lack of studies that focus on Malay-English code-mixing analytics and ii) lack of studies investigating various mood classes via the code-mixing approach.
Keywords
machine learning; mental health prediction; code switching analytics; systematic review; accuracy measurement;
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