Explainable Artificial Intelligence-Based Competitive Factor Identification

被引:5
作者
Han, Juhee [1 ]
Lee, Younghoon [2 ]
机构
[1] Seoul Natl Univ Sci & Technol, Dept Data Sci, 232 Gongneung Ro, Seoul 01811, South Korea
[2] Seoul Natl Univ Sci & Technol, Dept Ind Engn, 232 Gongneung Ro, Seoul 01811, South Korea
基金
新加坡国家研究基金会;
关键词
XAI; LRP; competitor analysis; competitive factors; mobile; ONLINE; EXTRACTION; REVIEWS; NETWORK;
D O I
10.1145/3451529
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Competitor analysis is an essential component of corporate strategy, providing both offensive and defensive strategic contexts to identify opportunities and threats. The rapid development of social media has recently led to several methodologies and frameworks facilitating competitor analysis through online reviews. Existing studies only focused on detecting comparative sentences in review comments or utilized low-performance models. However, this study proposes a novel approach to identifying the competitive factors using a recent explainable artificial intelligence approach at the comprehensive product feature level. We establish a model to classify the review comments for each corresponding product and evaluate the relevance of each keyword in such comments during the classification process. We then extract and prioritize the keywords and determine their competitiveness based on relevance. Our experiment results show that the proposed method can effectively extract the competitive factors both qualitatively and quantitatively.
引用
收藏
页数:11
相关论文
共 40 条
[1]  
Adom Alex Yaw., 2016, Journal of Resources Development and Management, V24, P116
[2]   Deriving the Pricing Power of Product Features by Mining Consumer Reviews [J].
Archak, Nikolay ;
Ghose, Anindya ;
Ipeirotis, Panagiotis G. .
MANAGEMENT SCIENCE, 2011, 57 (08) :1485-1509
[3]  
Boniface O, 2017, JCMAN, V14, P385
[4]   SenticNet 6: Ensemble Application of Symbolic and Subsymbolic AI for Sentiment Analysis [J].
Cambria, Erik ;
Li, Yang ;
Xing, Frank Z. ;
Poria, Soujanya ;
Kwok, Kenneth .
CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, 2020, :105-114
[5]  
Cambria E, 2018, AAAI CONF ARTIF INTE, P1795
[6]   Affective Computing and Sentiment Analysis [J].
Cambria, Erik .
IEEE INTELLIGENT SYSTEMS, 2016, 31 (02) :102-107
[7]   Student Research Abstract: Multi-Document Text Summarization for Competitor Intelligence: A Methodology based on Topic Identification and Artificial Bee Colony Optimization [J].
Chakraborti, Swapnajit .
30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II, 2015, :1110-1111
[8]   RE-SWOT: From User Feedback to Requirements via Competitor Analysis [J].
Dalpiaz, Fabiano ;
Parente, Micaela .
REQUIREMENTS ENGINEERING: FOUNDATION FOR SOFTWARE QUALITY (REFSQ 2019), 2019, 11412 :55-70
[9]   Identifying competitors through comparative relation mining of online reviews in the restaurant industry [J].
Gao, Song ;
Tang, Ou ;
Wang, Hongwei ;
Yin, Pei .
INTERNATIONAL JOURNAL OF HOSPITALITY MANAGEMENT, 2018, 71 :19-32
[10]   Analyzing the discriminative attributes of products using text mining focused on cosmetic reviews [J].
Guen, Kim Sung ;
Juyoung, Kang .
INFORMATION PROCESSING & MANAGEMENT, 2018, 54 (06) :938-957