Analysing Cloud Services Reviews Using Opining Mining
Analysing Cloud Services Reviews Using Opining Mining
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DOI：
10.1109/aina.2017.173
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发表时间：
2017-03
期刊：
影响因子：
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通讯作者：
A. Alkalbani;Lekhaben Gadhvi;B. Patel;F. Hussain;Ahmed Mohamed Ghamry;O. Hussain
中科院分区：
文献类型：
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作者：
A. Alkalbani;Lekhaben Gadhvi;B. Patel;F. Hussain;Ahmed Mohamed Ghamry;O. Hussain
人们越来越有兴趣在网络平台上分享产品和服务的体验，社交媒体为产品和服务提供商了解消费者的需求和期望开辟了一条途径。本文探讨了云消费者的评论，这些评论反映了消费者对云服务的体验。使用情感分析对约 6,000 名云服务用户的评论进行分析，以确定每个评论的态度，并确定所表达的意见是正面、负面还是中立。分析使用了两种数据挖掘工具KNIME和RapidMiner，并对结果进行了比较。我们在本研究中开发了四种预测模型来预测用户评论的情绪。所提出的模型基于四种监督机器学习算法：K 最近邻 (k-NN)、朴素贝叶斯、随机树和随机森林。结果表明，随机森林预测的准确率达到 97.06%，这使得该模型成为比其他三个模型更好的预测模型。
There is increasing interest in sharing the experience of products and services on the web platform, and social media has opened a way for product and service providers to understand their consumers needs and expectations. This paper explores reviews by cloud consumers that reflect consumers experiences with cloud services. The reviews of around 6,000 cloud service users were analysed using sentiment analysis to identify the attitude of each review, and to determine whether the opinion expressed was positive, negative, or neutral. The analysis used two data mining tools, KNIME and RapidMiner, and the results were compared. We developed four prediction models in this study to predict the sentiment of users reviews. The proposed model is based on four supervised machine learning algorithms: K-Nearest Neighbour (k-NN), Nave Bayes, Random Tree, and Random Forest. The results show that the Random Forest predictions achieve 97.06% accuracy, which makes this model a better prediction model than the other three.