Discovering Topics of Interest on Steam Community Using an LDA Approach

被引：5
|
作者：
Yu, Yang [1 ]
Nguyen, Ba-Hung [2 ]
Yu, Fangyu [1 ]
Huynh, Van-Nam [1 ]
机构：
[1] Japan Adv Inst Sci & Technol, Nomi, Japan
[2] Thai Binh Duong Univ, Nha Trang, Vietnam
来源：
ADVANCES IN THE HUMAN SIDE OF SERVICE ENGINEERING (AHFE 2021) | 2021年 / 266卷
关键词：
Esports reviews; Steam; Topic modeling; Collaborative filtering; REVIEWS; ESPORTS; GAME;
D O I：
10.1007/978-3-030-80840-2_59
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Reviews from players regarding different characteristics of an esports game are one of the worthiest sources for the developers to enhance their services or adjust operating strategy. However, little research has been conducted on detecting esports players' favorite topics dealing with topic modeling. Thus, this paper aims to use a data mining approach to analyze community data in the games domain available on Steam. We collected more than 1.2 million English reviews from four esports games up to August 2020 on Steam. Our contributions in this paper are: (i) we manually build a dataset by filtering out high-quality esports reviews, (ii) we then infer and group reviews into 3 groups with 19 topics, and (iii) we add more contributions to finding the emerging opinions of esports players towards the different topics of esports reviews, which might benefit further research on understanding esports reviews.
引用
收藏
页码：510 / 517
页数：8
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