Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels
The primary objective of this research is to develop and implement an artificial intelligence (AI) approach for the detection and classification of mental breakdowns in literary texts. The study employs text analytics techniques, utilizing natural language processing (NLP) to extract and analyze dat...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-01-01
|
| Series: | Social Sciences and Humanities Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590291123001638 |
| _version_ | 1797374181838422016 |
|---|---|
| author | Nur Anis Liyana Mohd Amram Pantea Keikhosrokiani Moussa Pourya Asl |
| author_facet | Nur Anis Liyana Mohd Amram Pantea Keikhosrokiani Moussa Pourya Asl |
| author_sort | Nur Anis Liyana Mohd Amram |
| collection | DOAJ |
| description | The primary objective of this research is to develop and implement an artificial intelligence (AI) approach for the detection and classification of mental breakdowns in literary texts. The study employs text analytics techniques, utilizing natural language processing (NLP) to extract and analyze data from six novels written by Afghan and Pakistani diasporic writers. The aim is to identify and classify the topics and sentiments related to depression in the selected narratives. To achieve these objectives, four algorithms for topic modelling are utilized, namely Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI), Hierarchical Dirichlet Process (HDP), and Non-negative Matrix Factorization (NMF). Additionally, a rule-based technique is applied for sentiment analysis using two Python libraries, VADER and TextBlob. For the classification of depression, four machine learning models are employed: Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The results indicate that HDP has the highest score in topic modelling with a score of 0.79. Furthermore, Vader provides more insightful sentiment analysis results. With a classification model accuracy of 68%, Naïve Bayes outperforms the other machine learning models. The findings suggest that the proposed model can efficiently predict all classes of depression, particularly when the dataset is balanced. |
| first_indexed | 2024-03-08T19:01:09Z |
| format | Article |
| id | doaj.art-bc4f8b84fd6b4bd3a131879e8e95f7d8 |
| institution | Directory Open Access Journal |
| issn | 2590-2911 |
| language | English |
| last_indexed | 2024-03-08T19:01:09Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Social Sciences and Humanities Open |
| spelling | doaj.art-bc4f8b84fd6b4bd3a131879e8e95f7d82023-12-28T05:18:55ZengElsevierSocial Sciences and Humanities Open2590-29112023-01-0181100558Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novelsNur Anis Liyana Mohd Amram0Pantea Keikhosrokiani1Moussa Pourya Asl2School of Computer Sciences, Universiti Sains Malaysia, Penang, MalaysiaSchool of Computer Sciences, Universiti Sains Malaysia, Penang, Malaysia; Corresponding author.School of Humanities, Universiti Sains Malaysia, Penang, MalaysiaThe primary objective of this research is to develop and implement an artificial intelligence (AI) approach for the detection and classification of mental breakdowns in literary texts. The study employs text analytics techniques, utilizing natural language processing (NLP) to extract and analyze data from six novels written by Afghan and Pakistani diasporic writers. The aim is to identify and classify the topics and sentiments related to depression in the selected narratives. To achieve these objectives, four algorithms for topic modelling are utilized, namely Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI), Hierarchical Dirichlet Process (HDP), and Non-negative Matrix Factorization (NMF). Additionally, a rule-based technique is applied for sentiment analysis using two Python libraries, VADER and TextBlob. For the classification of depression, four machine learning models are employed: Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), and Support Vector Machine (SVM). The results indicate that HDP has the highest score in topic modelling with a score of 0.79. Furthermore, Vader provides more insightful sentiment analysis results. With a classification model accuracy of 68%, Naïve Bayes outperforms the other machine learning models. The findings suggest that the proposed model can efficiently predict all classes of depression, particularly when the dataset is balanced.http://www.sciencedirect.com/science/article/pii/S2590291123001638Machine learningSentiment analysisTopic modellingDiasporic novelsDepression |
| spellingShingle | Nur Anis Liyana Mohd Amram Pantea Keikhosrokiani Moussa Pourya Asl Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels Social Sciences and Humanities Open Machine learning Sentiment analysis Topic modelling Diasporic novels Depression |
| title | Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels |
| title_full | Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels |
| title_fullStr | Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels |
| title_full_unstemmed | Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels |
| title_short | Artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels |
| title_sort | artificial intelligence approach for detection and classification of depression among refugees in selected diasporic novels |
| topic | Machine learning Sentiment analysis Topic modelling Diasporic novels Depression |
| url | http://www.sciencedirect.com/science/article/pii/S2590291123001638 |
| work_keys_str_mv | AT nuranisliyanamohdamram artificialintelligenceapproachfordetectionandclassificationofdepressionamongrefugeesinselecteddiasporicnovels AT panteakeikhosrokiani artificialintelligenceapproachfordetectionandclassificationofdepressionamongrefugeesinselecteddiasporicnovels AT moussapouryaasl artificialintelligenceapproachfordetectionandclassificationofdepressionamongrefugeesinselecteddiasporicnovels |