TY - GEN
T1 - Predictions on Usefulness and Popularity of Online Reviews
T2 - 24th International Conference on Human-Computer Interaction, HCII 2022
AU - Shou, Minghuan
AU - Bao, Xueqi
AU - Yu, Jie
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - This paper aims to propose an effective method to locate valuable reviews of mobile phones for older adults. After collecting the online reviews of mobile phones for older adults from JD mall, we propose a three-step framework. Firstly, Topic Modeling models and linguistic inquiry and word count (LIWC) methods are employed to extract latent topics. Secondly, regression models are used to examine the effect of variables obtained from the first step on the popularity (number of replies) and usefulness (number of helpful counts). Thirdly, seven machine learning models are adopted to predict the popularity and usefulness of online reviews. The results indicate that although older adults are more interested in the exterior, sound, money, and communication functions of mobile phones, they still care about the touch feel, work, and leisure functions. In addition, Random Forest performs the best in predicting the popularity and usefulness of online reviews. The findings can help e-commerce platforms and merchants identify the needs of the targeted consumers, predict which reviews will get more attention, and provide some early responses to some questions.
AB - This paper aims to propose an effective method to locate valuable reviews of mobile phones for older adults. After collecting the online reviews of mobile phones for older adults from JD mall, we propose a three-step framework. Firstly, Topic Modeling models and linguistic inquiry and word count (LIWC) methods are employed to extract latent topics. Secondly, regression models are used to examine the effect of variables obtained from the first step on the popularity (number of replies) and usefulness (number of helpful counts). Thirdly, seven machine learning models are adopted to predict the popularity and usefulness of online reviews. The results indicate that although older adults are more interested in the exterior, sound, money, and communication functions of mobile phones, they still care about the touch feel, work, and leisure functions. In addition, Random Forest performs the best in predicting the popularity and usefulness of online reviews. The findings can help e-commerce platforms and merchants identify the needs of the targeted consumers, predict which reviews will get more attention, and provide some early responses to some questions.
KW - Mobile phone
KW - Older adult
KW - Online review
UR - http://www.scopus.com/inward/record.url?scp=85140715434&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-17615-9_33
DO - 10.1007/978-3-031-17615-9_33
M3 - Conference contribution
AN - SCOPUS:85140715434
SN - 9783031176142
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 475
EP - 489
BT - HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction - 24th International Conference on Human-Computer Interaction, HCII 2022, Proceedings
A2 - Kurosu, Masaaki
A2 - Yamamoto, Sakae
A2 - Mori, Hirohiko
A2 - Soares, Marcelo M.
A2 - Rosenzweig, Elizabeth
A2 - Marcus, Aaron
A2 - Rau, Pei-Luen Patrick
A2 - Harris, Don
A2 - Li, Wen-Chin
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 26 June 2022 through 1 July 2022
ER -