基于Spark和机器学习的中国社交媒体数据医学分析

Siqi Peng
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引用次数: 0

摘要

社交媒体承载着海量的各类实时数据,在大数据时代的数据分析中发挥着重要作用。医务人员和普通民众之间的知识可以通过社交媒体进行普及和交流。同时,在社交媒体上收集和利用医疗数据,可以有效地掌握公共卫生状况,为改善人们的健康状况提供更好的帮助。本文从医疗健康的角度出发,利用中国最大的公共社交媒体微博获取数据进行分析。本研究在Spark框架下进行,使用朴素贝叶斯、随机森林和两种不同的特征提取方法对数据进行清洗、预处理和分类。并利用准确率和F1评分对模型进行评价,寻找最合适的方法。本研究结果表明,通过微博获取的特定年龄段人群的数据对公众认知和现状有很好的参考价值,有利于掌握疾病的趋势。
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Medical Analysis of Social Media Data Based on Spark and Machine Learning in China
Social media embracing a huge amount of real-time data of all kinds plays an important role in data analysis in the era of big data. Knowledge between medical workers and ordinary people can be popularized and exchanged via social media. At the same time, the collection and utilization of medical data on social media can effectively grasp the public health situation and provide better help to improve people's health status. From the perspective of medical care and health, this paper uses Weibo, the largest public social media in China, to obtain data for analysis. The study was developed under the Spark framework, using naive Bayes, random forest and two different feature extraction methods to clean, pre-process and classify data. Furthermore, the accuracy rate and F1 Score were used to evaluate the model, to find the most appropriate method. The result of this research shows that the data obtained from Weibo within certain age groups has a good reference value in the public awareness and current situation, and are good for grasping the trend of diseases.
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