从客户评论中提取功能,使用增强规则
Rajeswary Santhiran1, Kasturi Dewi Varathan1, Yin Kia Chiam2
1Department of Information Systems, Faculty of Computer Science & Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia.
PeerJ. Computer science
|March 4, 2024
概括
这项研究通过使用新的顺序模式规则来改进客户评论的明确特征提取来增强意见挖掘. 更新的方法提高了精度,回忆和F-测量,更好地捕捉客户的期望.
科学领域:
- 自然语言处理自然语言处理.
- 情绪分析 情绪分析
- 提取信息 提取信息
背景情况:
- 意见挖掘对于了解客户情绪和购买决策至关重要.
- 从非结构化评论中提取意见特征是具有挑战性的,因为语言错误和现有的模式规则的局限性.
- 当前的方法往往错过了相关的特征,而不是严格的名词或形容词.
研究的目的:
- 为了提高从产品审查文件中提取明确特征的性能.
- 通过使用顺序模式规则来识别和提取相关意见的特征.
- 解决特征提取现有规则中的局限性.
主要方法:
- 提出了一种采用顺序模式规则来提取特征和意见的方法.
- 开发了16个新的模式规则,与25个现有规则相结合,共计41个规则.
- 对五个数据集的方法进行了评估.
主要成果:
- 新一套16条规则显著改善了特征提取.
- 实现了0.91的平均精度,0.88的回忆和0.89.89的F测量.
- 在提取以前被忽视的特征方面表现出有效性.
结论:
- 该研究成功地提高了论挖掘中的明确特征提取.
- 拟议的顺序模式规则有效地解决了现有方法中的差距.
- 改进的方法可以更准确地了解客户对评论的期望.
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