使用机器学习算法衡量机场服务质量

Mohammed Salih Homaid, I. Moulitsas
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引用次数: 0

摘要

机场行业是一个竞争激烈的市场,在过去二十年中迅速扩张。机场管理层通常采用传统的方法来衡量乘客满意度,例如用户调查和专家意见,这需要时间和精力来分析。最近,人们非常关注使用机器学习技术和情感分析来衡量乘客满意度水平。情感分析可以使用一系列不同的方法来实现。然而,仍然不确定哪种技术更适合于识别特定主题领域或数据集的情感。在本文中,我们使用五种不同的算法,即逻辑回归,XGBoost,支持向量机,随机森林和Naïve贝叶斯来分析航空旅客的情绪。我们通过SKYTRAX网站获得数据集,该网站收集了大约600个机场的评论。我们应用了一些预处理步骤,例如通过使用术语频率逆文档频率将文本评论转换为数字形式。我们还使用NLTK停词列表从文本中删除停词。我们使用准确性、精密度、召回率和F1_score性能指标来评估我们的结果。我们的分析表明,与其他算法相比,XGBoost提供了最准确的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Measuring Airport Service Quality Using Machine Learning Algorithms
The airport industry is a highly competitive market that has expanded quickly during the last two decades. Airport management usually measures the level of passenger satisfaction by applying the traditional methods, such as user surveys and expert opinions, which require time and effort to analyse. Recently, there has been considerable attention on employing machine learning techniques and sentiment analysis for measuring the level of passenger satisfaction. Sentiment analysis can be implemented using a range of different methods. However, it is still uncertain which techniques are better suited for recognising the sentiment for a particular subject domain or dataset. In this paper, we analyse the sentiment of air travellers using five different algorithms, namely Logistic Regression, XGBoost, Support Vector Machine, Random Forest and Naïve Bayes. We obtain our data set through the SKYTRAX website which is a collection of reviews of around 600 airports. We apply some pre-processing steps, such as converting the textual reviews into numerical form, by using the term frequency-inverse document frequency. We also remove stopwords from the text using the NLTK list of stopwords. We evaluate our results using the accuracy, precision, recall and F1_score performance metrics. Our analysis shows that XGBoost provides the most accurate results when compared with other algorithms.
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