Year: 2024
Type: article
Abstract: Designing securities applications for mobile devices is challenging due to their inherent complexity, necessitating improvement through the analysis of online reviews. However, research applying deep learning techniques to the sentiment analysis of Korean text remains limited. This study explores the use of Aspect-Based Sentiment Analysis (ABSA) as an effective alternative to traditional user research methods for securities application design. By analyzing large volumes of text-based user review data of Korean securities applications, the study identifies critical elements like "update", "screen", "chart", "login", "access", "authentication", "account", and "transaction", revealing nuanced user sentiments through techniques such as PMI, SVD, and Word2Vec. ABSA offers deeper insights compared to overall ratings, uncovering hidden areas of dissatisfaction despite positive biases in reviews. This research demonstrates the scalability and cost-effectiveness of ABSA in mobile-application design research.
Source: Behavioral Sciences
DOI: 10.3390/bs14090814
Authors: Minji Kim, Subeen Kim, Yoonha Park, Sangwoo Bahn, Sung Hee Ahn, Bhavadharani NambiNarayanan
Institutions: Kyung Hee University, Namseoul University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,501
OpenAlex ID: W4402513433