Recent developments in real estate brokerage platforms have enabled residents to provide subjective reviews, which have immense value as subjective assessments and suggestions for architects. This study suggests a deep-learning-based natural language sentiment classification model to analyse reviews. Morpheme analysis and word embedding for ‘KoNLPy’ and ‘Word2vec’ were structured for pre-processing, and a long short-term memory network was used to process review data. Total 5974 review data were used in this study. Among the various active online platforms for real estate brokerage, platforms that provide online users with the ability to write reviews of their living spaces were crawled. The review data were classified as ‘positive’ or ‘negative’ by label and as ‘Apartment’ or ‘Non-Apartment’ by housing type. The model developed in this study is expected to increase in value as more online platforms appear in the future and the volume of natural language data generated by those platforms increases.

房地产经纪平台的最新发展使居民能够提供主观评论，这对于建筑师作为主观评估和建议具有巨大价值。这项研究提出了一种基于深度学习的自然语言情感分类模型来分析评论。'KoNLPy' 和 'Word2vec' 的词素分析和词嵌入结构用于预处理，并使用长短期记忆网络处理评论数据。本研究共使用了 5974 篇评论数据。在众多活跃的房地产经纪在线平台中，爬取了为在线用户提供撰写居住空间评论能力的平台。审查数据按标签分类为“正面”或“负面”，并按住房类型分类为“公寓”或“非公寓”。