The Internet of Things (IoT) plays an important role in helping the hotel industry increase customer satisfaction while maintaining affordable costs. IoT consumers review and rate the hotels online. The ratings are based on the Value, Apartment, Site, Sanitation, Front Desk, Facility, Professional Provision, Internet, and Packing. Traditional systems that predict hotel ratings with minimum accuracy create complexity through their analysis of the ratings. Thus, the effective deep learning techniques are used to analyze the reviews in order to help consumers choose better hotels. In this paper, different classification algorithms, such as convolutional neural network-based deep learning (CNN-DL), support vector machine network-based deep learning are applied to predict attributes. The system utilizes the TripAdvisor site, which is a well-known America dataset for examining system efficiency. The experimental results show that the CNN-DL algorithm has better classification accuracy and a lower error rate as compared to other algorithms.

中文翻译：

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使用传统神经网络从酒店行业中的IoT消费者评论进行属性预测：深度学习技术

物联网（IoT）在帮助酒店业提高客户满意度并保持可负担的成本方面发挥着重要作用。物联网消费者在线评论和评价酒店。评级基于价值，公寓，场地，卫生设施，前台，设施，专业用品，互联网和包装。传统的以最低的准确性预测酒店评分的系统会通过对评分的分析来创建复杂性。因此，有效的深度学习技术可用于分析评论，以帮助消费者选择更好的酒店。本文将基于卷积神经网络的深度学习（CNN-DL），基于支持向量机网络的深度学习等不同的分类算法用于预测属性。该系统利用TripAdvisor网站，这是一个著名的America数据集，用于检查系统效率。实验结果表明，与其他算法相比，CNN-DL算法具有更好的分类精度和较低的错误率。