论文标题
基于主观指标的云市场绩效预测
Subjective Metrics-based Cloud Market Performance Prediction
论文作者
论文摘要
本文探讨了一种有效的机器学习方法，以根据社交媒体来预测云消费者，提供者和投资者的云市场绩效。我们确定了一组全面的主观指标，这些指标可能会通过文献调查影响云市场的表现。我们使用流行的情感分析技术来处理从社交媒体收集的客户评论。云市场收入增长被选为云市场业绩的指标。我们认为亚马逊网络服务的收入增长是我们实验的利益相关者。选择了三种机器学习模型：线性回归，人工神经网络和支持向量机。将这些模型与时间序列预测模型进行了比较。我们发现，一组主观指标能够改善所有模型的预测性能。与其他模型相比，支持向量机显示了最佳的预测结果。
This paper explores an effective machine learning approach to predict cloud market performance for cloud consumers, providers and investors based on social media. We identified a set of comprehensive subjective metrics that may affect cloud market performance via literature survey. We used a popular sentiment analysis technique to process customer reviews collected from social media. Cloud market revenue growth was selected as an indicator of cloud market performance. We considered the revenue growth of Amazon Web Services as the stakeholder of our experiments. Three machine learning models were selected: linear regression, artificial neural network, and support vector machine. These models were compared with a time series prediction model. We found that the set of subjective metrics is able to improve the prediction performance for all the models. The support vector machine showed the best prediction results compared to the other models.
