融合多模态数据的酒店在线评论有效数据识别研究

Research on Effective Data Recognition of Hotel Online Reviews Based on Multi-modal Data

酒店在线评论具有数据量大、包含文本、图片等多模态数据的特点,对在线酒店评论信息分析的准确性会产生一定影响。本文探究基于酒店消费者生成的酒店在线评论信息有效性识别问题,从数据分析的角度出发,对酒店在线评论中的多模态信息进行融合,进行有效性识别处理。以酒店真实在线评论中的图文数据进行多模态语义融合,同时使用机器学习和深度学习方法识别出积极评论和消极评论。实验证明,本文提出的方法可以很好地提取出评论中的影响因素,并且对评论的分类效果较好。

Hotel online reviews have the characteristics of large amount of data, including text, pictures and other multi-modal data, which will have a certain impact on the accuracy of online hotel review information analysis. This paper explores the problem of identifying the validity of hotel online review information generated by hotel consumers. From the perspective of data analysis, the multi-modal information in hotel online reviews is fused and processed for validity identification. Multi-modal semantic fusion is carried out with the graphic data in real online reviews of hotels, and machine learning and deep learning methods are used to identify positive and negative reviews. Experiments have proved that the method proposed in this paper can extract the influencing factors in comments very well, and has a better classification effect on comments.