论文标题
快乐还是脾气暴躁？一种机器学习方法来分析航空公司乘客推文的情感
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets
论文作者
论文摘要
作为最广泛的社交网络服务之一，Twitter截至2022年，Twitter拥有超过3亿个活跃用户。在其众多功能中，Twitter现在是消费者对产品或体验的意见的首选平台之一，包括商业航空公司提供的飞行服务。这项研究旨在通过分析使用机器学习方法提及航空公司的推文的观点来衡量客户满意度。从Twitter的API中检索相关的推文，并通过令牌化和矢量化处理。之后，这些处理过的向量被传递到预训练的机器学习分类器中以预测情感。除了情感分析外，我们还对收集的推文进行了词汇分析，以建模关键字的频率，这些频率提供了有意义的上下文以促进情感的解释。然后，我们应用时间序列方法，例如鲍林（Bollinger）频段来检测情绪数据中的异常。使用从2022年1月到7月的历史记录，我们的方法被证明能够捕捉乘客情绪突然而重大变化。这项研究有可能发展为可以帮助航空公司以及其他几个面向客户的企业的应用程序，有效地检测到客户情绪的突然变化，并采取足够的措施来抵消他们。
As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. This study aims to measure customer satisfaction by analyzing sentiments of Tweets that mention airlines using a machine learning approach. Relevant Tweets are retrieved from Twitter's API and processed through tokenization and vectorization. After that, these processed vectors are passed into a pre-trained machine learning classifier to predict the sentiments. In addition to sentiment analysis, we also perform lexical analysis on the collected Tweets to model keywords' frequencies, which provide meaningful contexts to facilitate the interpretation of sentiments. We then apply time series methods such as Bollinger Bands to detect abnormalities in sentiment data. Using historical records from January to July 2022, our approach is proven to be capable of capturing sudden and significant changes in passengers' sentiment. This study has the potential to be developed into an application that can help airlines, along with several other customer-facing businesses, efficiently detect abrupt changes in customers' sentiments and take adequate measures to counteract them.
