Subjective Metrics-based Cloud Market Performance Prediction
This work addresses performance prediction for cloud consumers, providers, and investors, but it is incremental as it applies existing methods to new data.
The paper tackled predicting cloud market performance using subjective metrics from social media, finding that these metrics improved prediction across models, with support vector machine achieving the best results.
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.