Why Do Users Choose a Hotel over Others? Review Analysis Using Interpretation Method of Machine Learning Models

• Published in: 2020 5th IEEE International Conference on Big Data Analytics (ICBDA) pp. 354 - 362
• Main Authors: Onogawa, Takayuki, Orihara, Ryohei, Sei, Yuichi, Tahara, Yasuyuki, Ohsuga, Akihiko
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-05-2020
• Subjects: Data models; Feature extraction; Interpretability; Machine learning; Natural language processing; Review analysis; Reviews; Sentiment analysis
• DOI: 10.1109/ICBDA49040.2020.9101264

To date, existing research has attempted to extract user opinions relating to products and services by differentiating the products and services through their characteristics. However, it is difficult to obtain the characteristics of similar products and services in these studies. So-called competitive products and services are actually quite similar, though they might vary in their strengths and weaknesses across competitors. These advantages and disadvantages are essential for users when selecting products and services. On the other hand, in recent years, researchers have interpreted the output of machine learning models so that humans can understand the reason of the output. LIME and SP-LIME are typical approaches used in the literature. In this paper, we propose a new method using LIME and SP-LIME to compare the characteristics of the services of three competing hotels and investigate appropriate parameters for our method. We try to extract descriptive words for each competitor from review texts, taking a Japanese business hotel market and global game console market as examples.