Imputing sentiment intensity for SaaS service quality aspects using T-nearest neighbors with correlation-weighted Euclidean distance

摘要

The rapid, increasing adoption of businesses to deliver their services in Software as a Service (SaaS) products in the marketplace presents selection challenges to users. Recently, major cloud service providers such as Amazon Web Services and Microsoft Azure have introduced well-architected frameworks that assess SaaS products in different pillars (also referred to herein as features). Furthermore, customers leave feedback on these features after using SaaS products. However, they do not provide feedback on all the features of a product, which renders the reviews unusable to prospective users needing to assess a product’s quality before committing to it. Our study addresses this drawback by imputing or inferring the intensity of the customer’s feedback on features that they do not mention in their reviews. Specifically, we propose threshold-based nearest neighbors (T-NN) as an extension of the conventional k-nearest neighbor approach to determine the missing sentiment intensity score of a feature from the values of its other features. We evaluate the proposed approach in two different systems and compare our results with seven other data imputation techniques. The results show that the proposed T-NN approach performs better than the other imputation approaches on the SaaS sentiment dataset.