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人们对快餐的看法:对快餐餐厅的意见分析和LDA建模,使用非结构化的推文
Muhammad Mujahid1, Furqan Rustam2, Fahad Alasim3
1Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Raheem Yar Khan, Pakistan.
PeerJ. Computer science
|June 22, 2023
概括
包括BiLSTM+GRU在内的深层组合模型在分析来自社交媒体的快餐客户评论方面优于基于词典的方法. BiLSTM+GRU获得了95.31%的准确性,确定了带有最负面反的地铁.
科学领域:
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
- 社交媒体分析
背景情况:
- 社交媒体上的客户评论极大地影响了餐饮选择.
- 手动分析客户反是耗时和低效的.
- 现有的研究往往忽视了快餐行业情绪分析的深层合奏模型.
研究的目的:
- 评估深层合奏模型的表现,以分析快餐客户评论的情感分析.
- 将深层集合模型与基于词典的方法进行比较.
- 通过使用隐性迪里克莱特分配 (LDA) 建模,识别与顶级快餐品牌相关的普遍主题和情绪.
主要方法:
- 使用了来自肯德基,披萨小屋,麦当劳,汉堡王和地铁的自我收集的推特.
- 实施的深层组合模型:Bi长期短期记忆和封闭循环单元 (BiLSTM+GRU),LSTM+GRU,GRU+循环神经网络 (GRU+RNN) 和BiLSTM+RNN.
- 采用隐性迪里克莱特分配 (LDA) 来进行主题分析,并将结果与基于词典的方法进行比较.
主要成果:
- 深层组合模型在情感分类中显著超过了基于词典的方法.
- 对于三类情绪问题,BiLSTM+GRU模型实现了最高准确率 (95.31%).
- 隐性迪里克莱特分配显示,地铁收到了最负面的情绪,而49%的用户对快餐表达了中立的意见.
结论:
- 深度组合模型为分析快餐行业客户情绪提供了卓越的性能.
- BiLSTM+GRU是一个高度有效的模型,用于对社交媒体数据的情感分类.
- 像Subway这样的品牌需要有针对性的改进,以解决普遍存在的消费者负面反.
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