揭示情感:利用先进的人工智能模型全面分析 2017-2022 年阿拉伯语朝觐相关推文

Hanan M. Alghamdi
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摘要

情感分析在了解公众舆论和社交媒体趋势方面起着至关重要的作用。它涉及分析特定文本的情感基调和极性。当应用到阿拉伯语文本时,由于该语言的复杂形态、从右到左的文字以及表达情感时错综复杂的细微差别,这项任务变得尤其具有挑战性。社交媒体已成为个人表达情感的强大平台,尤其是针对宗教和文化事件。因此,研究朝觐背景下的情感分析已成为一个引人入胜的课题。本研究论文对六年来讨论年度朝觐的推文进行了全面的情感分析。通过结合使用机器学习和深度学习模型,本研究成功地对由阿拉伯语推文组成的大量数据集进行了情感分析。这一过程包括预处理、特征提取和情感分类。目的是揭示不同年份、每次朝觐活动之前、期间和之后与朝觐相关的普遍情绪。重要的是,本研究的结果表明,基于转换器的高级模型 BERT 在准确进行情感分类方面优于其他模型。这凸显了它在捕捉阿拉伯语文本固有的复杂性方面的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models
Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.
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