首页|Universitas Islam Negeri Researchers Discuss Findings in Machine Learning (Emoti onal Responses to Religious Conversion: Insights from Machine Learning)
Universitas Islam Negeri Researchers Discuss Findings in Machine Learning (Emoti onal Responses to Religious Conversion: Insights from Machine Learning)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators publish new report on ar tificial intelligence. According to news originating from the Universitas Islam Negeri by NewsRx correspondents, research stated, "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 s et analyzed in this paper comes from in-depth interviews with 12 mualaf/ newly c onverted Muslims from various backgrounds." Our news journalists obtained a quote from the research from Universitas Islam N egeri: "All recorded interviews were transcribed and filtered to remove any unne cessary or misaligned data to ensure that the data was fully aligned with the in terview questions. To analyze emotional changes, we utilize natural language pro cessing (NLP) algorithms, which enable us to extract and interpret emotional con tent from textual data sources, such as personal narratives. The analysis was pe rformed 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 competi tors, namely LS-EMO and UJ-Combi. The model also has great performance, with the overall average Macro-F1s for XLMRoBERTa- large, XLM-RoBERTa-base, and XLM-Twit ter-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 reason ing behind the emotions deemed pertinent and intriguing for our investigation. T his study showed that Sadness is the most dominant emotion, constituting 46.67% of the total emotions in the pre-conversion context."
Universitas Islam NegeriCyborgsEmerg ing TechnologiesMachine Learning
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