Subjective Metrics-based Cloud Market Performance Prediction

TL;DR

This paper presents a machine learning approach using subjective social media metrics to predict cloud market performance, demonstrating improved accuracy with SVM models, especially for AWS revenue growth.

Contribution

It introduces a novel set of subjective metrics from social media for cloud market prediction and compares multiple machine learning models for effectiveness.

Findings

Subjective metrics improve prediction accuracy.

Support vector machine outperforms other models.

Social media sentiment analysis is effective for market prediction.

Abstract

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.