  Sentiment analysis is the task of mining the authors' opinions about specific
entities. It allows organizations to monitor different services in real time
and act accordingly. Reputation is what is generally said or believed about
people or things. Informally, reputation combines the measure of reliability
derived from feedback, reviews, and ratings gathered from users, which reflect
their quality of experience (QoE) and can either increase or harm the
reputation of the provided services. In this study, we propose to perform
sentiment analysis on web microservices reviews to exploit the provided
information to assess and score the microservices' reputation. Our proposed
approach uses the Long Short-Term Memory (LSTM) model to perform sentiment
analysis and the Net Brand Reputation (NBR) algorithm to assess reputation
scores for microservices. This approach is tested on a set of more than 10,000
reviews related to 15 Amazon Web microservices, and the experimental results
have shown that our approach is more accurate than existing approaches, with an
accuracy and precision of 93% obtained after applying an oversampling strategy
and a resulting reputation score of the considered microservices community of
89%.
