使用基于词典的情感分析对阿拉伯文本框架的客户感知分析
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
|June 7, 2024
概括
这项研究引入了一个新的社交媒体的阿拉伯情绪分析框架 (LSAnArTe). 在分析消费者对咖啡产品的看法方面,LSAnArTe框架显著优于现有的工具.
科学领域:
- 自然语言处理 (NLP) 是一种自然语言处理.
- 计算语言学 计算语言学
- 社交媒体分析
背景情况:
- 由于全球社交媒体的覆盖范围,阿拉伯语情绪分析的需求日益增长.
- 现有的研究不足以解决阿拉伯消费者对咖啡等产品的看法.
- 需要强大的阿拉伯文本分析工具来理解消费者反.
研究的目的:
- 提出和验证一个全面的基于词典的阿拉伯文本情感分析 (LSAnArTe) 框架.
- 利用阿拉伯语社交媒体数据分析客户对咖啡和咖啡产品的看法.
- 评估LSAnArTe框架与现有工具的性能.
主要方法:
- 开发了LSAnArTe框架,使用AraSenTi字典和Qalasadi平台进行 lemmatization.
- 基于词典分数的分类词和句子情绪.
- 使用手动注释的数据集验证了框架,其中包括来自X (以前的Twitter) 的10,769条推文.
主要成果:
- 在情绪分析中,LSAnArTe框架实现了93.79%的高精度.
- 拟议的框架显著优于亚马逊Comprehend,该框架达到51.90%的准确性.
- 手动注释确保了框架验证的高准确性和可靠性.
结论:
- LSAnArTe框架提供了一个非常准确的解决方案,用于对社交媒体数据的阿拉伯情绪分析.
- 这项研究解决了分析阿拉伯客户对咖啡等产品的反的关键差距.
- 开发的框架为寻求了解阿拉伯语消费市场的企业提供了有价值的工具.
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