宗教皈依的情绪反应:机器学习的启示

Q1 Arts and Humanities
Achmad Maimun, Andi Bahtiar Semma
{"title":"宗教皈依的情绪反应:机器学习的启示","authors":"Achmad Maimun, Andi Bahtiar Semma","doi":"10.25217/0020236395500","DOIUrl":null,"url":null,"abstract":"This study aims to understand the feelings of newly converted Muslims when they narrated their pre- and post-conversion using the Machine Learning model and qualitative approach. The data set analyzed in this paper comes from in-depth interviews with 12 mualaf/ newly converted Muslims from various backgrounds. All recorded interviews were transcribed and filtered to remove any unnecessary or misaligned data to ensure that the data was fully aligned with the interview questions. To analyze emotional changes, we utilize natural language processing (NLP) algorithms, which enable us to extract and interpret emotional content from textual data sources, such as personal narratives. The analysis was performed in Google Colab and utilizing XLM-EMO, a fine-tuned multilingual emotion detection model that detects joy, anger, fear, and sadness emotions from text. The model was chosen because it supports Bahasa, as our interview was conducted in Bahasa. Furthermore, the model also has the best accuracy amongst its competitors, namely LS-EMO and UJ-Combi. The model also has great performance, with the overall average Macro-F1s for XLM-RoBERTa-large, XLM-RoBERTa-base, and XLM-Twitter-base are .86, .81, and .84. Furthermore, two psychologists compared emotion detection results from the XLM-EMO model to the raw input data, and an inductive content analysis was performed. This approach allowed us to identify the reasoning behind the emotions deemed pertinent and intriguing for our investigation. This study showed that Sadness is the most dominant emotion, constituting 46.67% of the total emotions in the pre-conversion context. On the other hand, joy emerges as the most dominant, constituting a substantial proportion of 57.73% among the emotions analyzed from post-conversion emotions data. Understanding the positive impact of religious conversion on emotions may inform mental health interventions and incorporate spiritual or religious elements into therapeutic approaches for individuals struggling with emotional issues, guiding individuals undergoing religious conversion and emphasizing the potential emotional benefits.","PeriodicalId":32996,"journal":{"name":"Islamic Guidance and Counseling Journal","volume":"33 4 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Emotional Responses to Religious Conversion: Insights from Machine Learning\",\"authors\":\"Achmad Maimun, Andi Bahtiar Semma\",\"doi\":\"10.25217/0020236395500\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study aims to understand the feelings of newly converted Muslims when they narrated their pre- and post-conversion using the Machine Learning model and qualitative approach. The data set analyzed in this paper comes from in-depth interviews with 12 mualaf/ newly converted Muslims from various backgrounds. All recorded interviews were transcribed and filtered to remove any unnecessary or misaligned data to ensure that the data was fully aligned with the interview questions. To analyze emotional changes, we utilize natural language processing (NLP) algorithms, which enable us to extract and interpret emotional content from textual data sources, such as personal narratives. The analysis was performed in Google Colab and utilizing XLM-EMO, a fine-tuned multilingual emotion detection model that detects joy, anger, fear, and sadness emotions from text. The model was chosen because it supports Bahasa, as our interview was conducted in Bahasa. Furthermore, the model also has the best accuracy amongst its competitors, namely LS-EMO and UJ-Combi. The model also has great performance, with the overall average Macro-F1s for XLM-RoBERTa-large, XLM-RoBERTa-base, and XLM-Twitter-base are .86, .81, and .84. Furthermore, two psychologists compared emotion detection results from the XLM-EMO model to the raw input data, and an inductive content analysis was performed. This approach allowed us to identify the reasoning behind the emotions deemed pertinent and intriguing for our investigation. This study showed that Sadness is the most dominant emotion, constituting 46.67% of the total emotions in the pre-conversion context. On the other hand, joy emerges as the most dominant, constituting a substantial proportion of 57.73% among the emotions analyzed from post-conversion emotions data. Understanding the positive impact of religious conversion on emotions may inform mental health interventions and incorporate spiritual or religious elements into therapeutic approaches for individuals struggling with emotional issues, guiding individuals undergoing religious conversion and emphasizing the potential emotional benefits.\",\"PeriodicalId\":32996,\"journal\":{\"name\":\"Islamic Guidance and Counseling Journal\",\"volume\":\"33 4 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Islamic Guidance and Counseling Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25217/0020236395500\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Arts and Humanities\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Islamic Guidance and Counseling Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25217/0020236395500","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Arts and Humanities","Score":null,"Total":0}
引用次数: 0

摘要

本研究旨在利用机器学习模型和定性方法了解新皈依穆斯林在讲述皈依前后的感受。本文分析的数据集来自对 12 名来自不同背景的穆拉夫/新皈依穆斯林的深入访谈。我们对所有访谈录音进行了转录和过滤,以删除任何不必要或不一致的数据,确保数据与访谈问题完全一致。为了分析情感变化,我们使用了自然语言处理(NLP)算法,该算法使我们能够从文本数据源(如个人叙述)中提取和解释情感内容。分析是在 Google Colab 中进行的,并使用了 XLM-EMO,这是一种经过微调的多语言情感检测模型,可从文本中检测出喜悦、愤怒、恐惧和悲伤等情感。之所以选择该模型,是因为它支持巴哈萨语,因为我们的访谈是用巴哈萨语进行的。此外,在 LS-EMO 和 UJ-Combi 这两个竞争对手中,该模型的准确率也是最高的。该模型还具有出色的性能,XLM-RoBERTa-large、XLM-RoBERTa-base 和 XLM-Twitter-base 的总体平均 Macro-F1s 分别为 0.86、0.81 和 0.84。此外,两位心理学家将 XLM-EMO 模型的情感检测结果与原始输入数据进行了比较,并进行了归纳内容分析。通过这种方法,我们确定了被认为与我们的调查相关且耐人寻味的情绪背后的原因。研究表明,悲伤是最主要的情绪,占转换前情绪总数的 46.67%。另一方面,在皈依后的情绪数据分析中,喜悦成为最主要的情绪,占 57.73% 的相当大比例。了解宗教皈依对情绪的积极影响,可为心理健康干预提供参考,并将精神或宗教元素融入治疗方法中,以帮助与情绪问题作斗争的个人,引导正在进行宗教皈依的个人,并强调其潜在的情绪益处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotional Responses to Religious Conversion: Insights from Machine Learning
This study aims to understand the feelings of newly converted Muslims when they narrated their pre- and post-conversion using the Machine Learning model and qualitative approach. The data set analyzed in this paper comes from in-depth interviews with 12 mualaf/ newly converted Muslims from various backgrounds. All recorded interviews were transcribed and filtered to remove any unnecessary or misaligned data to ensure that the data was fully aligned with the interview questions. To analyze emotional changes, we utilize natural language processing (NLP) algorithms, which enable us to extract and interpret emotional content from textual data sources, such as personal narratives. The analysis was performed in Google Colab and utilizing XLM-EMO, a fine-tuned multilingual emotion detection model that detects joy, anger, fear, and sadness emotions from text. The model was chosen because it supports Bahasa, as our interview was conducted in Bahasa. Furthermore, the model also has the best accuracy amongst its competitors, namely LS-EMO and UJ-Combi. The model also has great performance, with the overall average Macro-F1s for XLM-RoBERTa-large, XLM-RoBERTa-base, and XLM-Twitter-base are .86, .81, and .84. Furthermore, two psychologists compared emotion detection results from the XLM-EMO model to the raw input data, and an inductive content analysis was performed. This approach allowed us to identify the reasoning behind the emotions deemed pertinent and intriguing for our investigation. This study showed that Sadness is the most dominant emotion, constituting 46.67% of the total emotions in the pre-conversion context. On the other hand, joy emerges as the most dominant, constituting a substantial proportion of 57.73% among the emotions analyzed from post-conversion emotions data. Understanding the positive impact of religious conversion on emotions may inform mental health interventions and incorporate spiritual or religious elements into therapeutic approaches for individuals struggling with emotional issues, guiding individuals undergoing religious conversion and emphasizing the potential emotional benefits.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Islamic Guidance and Counseling Journal
Islamic Guidance and Counseling Journal Arts and Humanities-Religious Studies
CiteScore
1.20
自引率
0.00%
发文量
0
审稿时长
15 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信