A Comparative Study of Traditional and Emerging Commercial Districts in Cheongju City Using Big Data Sentiment Analysis = A Comparative Study of Traditional and Emerging Commercial Districts in Cheongju City Using Big Data Sentiment Analysis

다국어 초록 (Multilingual Abstract)

This study analyzes consumer sentiment―both positive and negative―toward two commercial districts in Cheongju, South Korea: Seongan-gil, a traditional downtown area, and Bokdae-dong, an emerging commercial district. Using big data sentiment analysis of online posts and news articles, the research aims to identify sentiment-related characteristics that distinguish traditional from emerging commercial districts, with implications for local commercial revitalization and sustainable urban development. A total of 1,967 news articles, blog posts, and online community posts published on major Korean portal sites between March 2021 and August 2023 were collected. After data cleaning, 430 relevant sentences were analyzed using the Textom program. The analytical procedures included word frequency analysis, TF-IDF analysis, and sentiment lexicon analysis. The findings reveal that both districts are primarily associated with positive sentiments. In Seongan-gil, positive perceptions stem from favorable evaluations of cafés and restaurants, customer service quality, and hopes for cultural regeneration initiatives. Conversely, negative sentiments are linked to parking difficulties and concerns over declining sales. For Bokdae-dong, positive sentiments are associated with modern store atmospheres, interior design, and innovative business types, while negative sentiments primarily relate to high prices and parking inconvenience.
Academically, this study enhances commercial district research by integrating TF-IDF-based text mining with sentiment analysis to capture both cognitive and affective dimensions of consumer perception. Empirically and practically, the findings demonstrate that online sentiment data can illuminate the distinct experiential characteristics of commercial districts at different stages of development, providing insights for developing targeted revitalization strategies for traditional and emerging commercial areas.