摘要
由一名新闻记者-机器人与机器学习的工作人员新闻编辑-每日新闻-调查人员发布了关于人工智能的新报告。根据NewsRx记者从伊斯兰大学传来的消息，研究表明：“本研究旨在通过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。本文分析的数据来自对12名来自不同背景的穆阿拉夫/新皈依穆斯林的深入访谈。”我们的新闻记者从伊斯兰大学的研究中获得了一句话：“所有记录的采访都被转录和过滤，以删除任何不必要的或错误的数据，以确保数据与内部问题完全一致。为了分析情绪变化，我们使用自然语言处理(NLP)算法，这使我们能够从文本数据来源提取和解释情绪内容。”该分析是在Google Colab中进行的，并利用XLM-EMO，这是一个微调的多语言情绪检测模型，可以从文本中检测快乐、愤怒、恐惧和悲伤情绪。由于我们的采访是用巴哈萨语进行的，因此选择该模型是因为它支持巴哈萨语。此外，该模型在LS-EMO和UJ-COMBI中也具有最好的准确性。XLM-Roberta-Large、XLM-Roberta-Base和XLM-Twitt Ter-Base的总体平均Macro-F1s分别为.86、.81和.84.。此外，两位心理学家将XLM-EMO模型的情绪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。这种方法使我们能够找出我们认为相关和有趣的情绪背后的原因。他的在转换前情境中占总情绪的46.67%。
Abstract
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."
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