LSTM-based QoE Evaluation for Web Microservices' Reputation Scoring

TL;DR

This paper presents an LSTM-based sentiment analysis method combined with a reputation scoring algorithm to evaluate the quality of web microservices based on user reviews, achieving high accuracy and precision.

Contribution

It introduces a novel approach integrating LSTM sentiment analysis with the NBR algorithm for microservice reputation scoring, validated on real-world review data.

Findings

Achieved 93% accuracy and precision in sentiment analysis.

Reputation scores correlated well with user reviews and feedback.

Method outperforms existing sentiment analysis approaches.

Abstract

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%.