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In recent years, data posted about real estate on the Internet is currently increasing. In this study, in order to analyze user needs for real estate, we focus on &#x201C;Mansion Community&#x201D; which is a Japanese bulletin board system (hereinafter referred to as BBS) about Japanese real estate. In our study, extraction of keywords is performed based on calculation of the entropy value of each word, and we used them as features in a machine learning classifier to analyze 6 million posts at &#x201C;Mansion Community&#x201D;. As a result, we achieved a 0.69 F-measure and found that the customers are particularly concerned about the facility of apartment, access, and price of an apartment.

Development of an entropy-based feature selection method and analysis of online reviews on real estate