利用 twitter 数据分析患者对医疗服务的满意度

Muhammad Usman, Muhammad Mujahid, F. Rustam, EmmanuelSoriano Flores, Juan Luís Vidal Mazón, Isabel de la Torre Díez, I. Ashraf
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摘要

在过去两年中,由于 COVID-19 的爆发,公众对医疗系统的关注度急剧上升。因此,医疗专业人员和医疗相关机构主动联系患者,寻求反馈意见,以分析、监督和提升医疗服务。这些意见和看法通常会在 Facebook、Instagram、Twitter 等社交媒体平台上分享。推特是最受欢迎和研究人员最常用的网络平台,可即时获取实时新闻、意见和讨论。其流行标签 (#) 和病毒性内容使其成为监测各种话题舆论的理想枢纽。我们使用 #healthcare、#healthcare services 和 #medical facilities 三个标签提取推文。此外,本研究还考虑了位置和推文情感分析。最近有几项研究使用 ML 和 DL 模型部署了 Twitter 数据集,但结果显示准确率较低。此外,这些研究没有进行广泛的比较分析,也缺乏验证。本研究解决了两个研究问题:第一,全球范围内人们对医疗服务的情感如何? 第二,机器学习和深度学习方法对医疗推文情感分类的效果如何?实验使用了几种著名的机器学习模型,包括支持向量机、逻辑回归、高斯天真贝叶斯、额外树分类器、k 近邻、随机森林、决策树和 AdaBoost。此外,本研究还提出了一种基于迁移学习的 LSTM-ETC 模型,该模型可有效预测医疗数据集中的客户满意度。结果表明,尽管 ETC 模型的准确率为 0.88,表现最佳,但所提出的模型的准确率却高达 0.95。由于积极情绪的比例大大高于消极情绪,因此人们主要对所提供的医疗服务感到满意。正面或负面情绪在通过客户反馈做出重要决策和提高质量方面发挥着至关重要的作用。
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Analyzing patients satisfaction level for medical services using twitter data
Public concern regarding health systems has experienced a rapid surge during the last two years due to the COVID-19 outbreak. Accordingly, medical professionals and health-related institutions reach out to patients and seek feedback to analyze, monitor, and uplift medical services. Such views and perceptions are often shared on social media platforms like Facebook, Instagram, Twitter, etc. Twitter is the most popular and commonly used by the researcher as an online platform for instant access to real-time news, opinions, and discussion. Its trending hashtags (#) and viral content make it an ideal hub for monitoring public opinion on a variety of topics. The tweets are extracted using three hashtags #healthcare, #healthcare services, and #medical facilities. Also, location and tweet sentiment analysis are considered in this study. Several recent studies deployed Twitter datasets using ML and DL models, but the results show lower accuracy. In addition, the studies did not perform extensive comparative analysis and lack validation. This study addresses two research questions: first, what are the sentiments of people toward medical services worldwide? and second, how effective are the machine learning and deep learning approaches for the classification of sentiment on healthcare tweets? Experiments are performed using several well-known machine learning models including support vector machine, logistic regression, Gaussian naive Bayes, extra tree classifier, k nearest neighbor, random forest, decision tree, and AdaBoost. In addition, this study proposes a transfer learning-based LSTM-ETC model that effectively predicts the customer’s satisfaction level from the healthcare dataset. Results indicate that despite the best performance by the ETC model with an 0.88 accuracy score, the proposed model outperforms with a 0.95 accuracy score. Predominantly, the people are happy about the provided medical services as the ratio of the positive sentiments is substantially higher than the negative sentiments. The sentiments, either positive or negative, play a crucial role in making important decisions through customer feedback and enhancing quality.
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