阿拉伯社交媒体文本的情感分析:一种机器学习方法来破译客户的感知
Ohud Alsemaree1, Atm S Alam1, Sukhpal Singh Gill1
1School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, UK.
Heliyon
|May 7, 2024
概括
这项研究引入了一种先进的阿拉伯情绪分析方法,用于咖啡产品评论,达到95%以上的准确性. 这种新方法提高了对社交媒体上的客户意见的理解.
科学领域:
- 人工智能的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 对竞争激烈的咖啡行业来说,客户情绪分析至关重要.
- 传统的市场分析方法难以捕捉消费者的微妙观点.
- 由于复杂的语言形态学,阿拉伯语情感分析具有挑战性.
研究的目的:
- 为咖啡产品开发一种精确有效的阿拉伯情绪分析方法.
- 从社交媒体数据了解客户对咖啡产品的看法.
- 帮助企业做出有关产品推广和改进的明智决策.
主要方法:
- 收集了各种咖啡产品的 10,646 条推特评论.
- 应用术语 频率-反向文档频率 (TF-IDF) 和最小冗余最大相关性 (MRMR) 用于特征提取.
- 利用了k-最近邻居,支持向量机,决策树和随机森林算法,用于情感分类.
主要成果:
- 使用硬投票组合实现了超过95.95%的准确性.
- 通过软投票组合达到94.51%的准确性.
- 对于阿拉伯语情绪分析来说,证明了预测准确度的提高.
结论:
- 开发的方法显著提高了对产品评价的阿拉伯情绪分析的精度.
- 这种方法为消费者对咖啡产品的看法提供了有价值的见解.
- 这些发现支持咖啡企业的数据驱动决策.
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