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%.

翻译：情感分析是从作者关于特定实体的意见中挖掘信息的任务。它使组织能够实时监控不同服务并采取相应行动。信誉是人们普遍对人或事物所说或相信的看法。非正式地说，信誉综合了从用户反馈、评论和评分中衍生的可靠性度量，这些度量反映了他们的体验质量（QoE），既可能提升也可能损害所提供服务的声誉。在本研究中，我们提出对Web微服务评论进行情感分析，利用所得信息评估和量化微服务的信誉。我们提出的方法使用长短期记忆（LSTM）模型执行情感分析，并采用网络品牌信誉（NBR）算法评估微服务的信誉分数。该方法在超过10,000条与15个亚马逊Web微服务相关的评论集上进行了测试，实验结果表明，我们的方法比现有方法更准确，在应用过采样策略后获得了93%的准确率和精确度，所考虑的微服务社区的最终信誉得分为89%。