Hotel bathrooms are one of the most important places in terms of customer satisfaction, and where the most complaints are reported. To share their experiences, guests rate hotels, comment, and share images of their positive or negative ratings. An important part of the room images shared by guests is related to bathrooms. Guests tend to prove their satisfaction or dissatisfaction with the bathrooms with images in their comments. These Positive or negative comments and visuals potentially affect the prospective guests. In this study, two different versions of a deep learning algorithm were designed to classify hotel bathrooms as satisfactory (good) or unsatisfactory (bad, when any defects such as dirtiness, deficiencies, malfunctions were present) by analyzing images. The best-performer between the two models was determined as a result of a series of extensive experimental studies. The models were trained for each of 144 combinations of 5 hyper-parameter sets with a data set containing more than 11 thousand bathroom images, specially created for this study. The "HotelBath" data set was shared also with the community with this study. Four different image sizes were taken into consideration: 128, 256, 512 and 1024 pixels in both directions. The classification performances of the models were measured with several metrics. Both algorithms showed very attractive performances even with many combinations of hyper-parameters. They can classify bathroom images with very high accuracy. Suh that the top algorithm achieved an accuracy of 92.4% and an AUC (area under the curve) score of 0.967. In addition, other metrics also proved the success...

翻译：酒店浴室是影响顾客满意度最重要的场所之一，也是投诉最集中的区域。为分享体验，住客会对酒店进行评分、发表评论并上传反映其正面或负面评价的图像。住客分享的房间图像中，相当一部分与浴室相关。住客倾向于通过图像在评论中证明其对浴室的满意或不满意。这些正面或负面的评论与视觉内容可能影响潜在顾客。本研究设计了两种不同版本的深度学习算法，通过图像分析将酒店浴室分类为满意（良好）或不满意（不良，即存在污垢、缺陷、故障等缺陷）。经过一系列广泛的实验研究，确定了两种模型中性能最优的算法。使用专门为本研究创建的包含超过11000张浴室图像的数据集，对5组超参数构成的144种组合分别训练模型。本研究还向学术界共享了"HotelBath"数据集。研究考虑了四种不同图像尺寸：128、256、512和1024像素（双向）。通过多种指标衡量模型的分类性能。两种算法即使在多种超参数组合下也展现出极具吸引力的表现，能够以极高准确率对浴室图像进行分类。其中最优算法达到92.4%的准确率和0.967的AUC（曲线下面积）得分。此外，其他指标也证实了该方法的成功...