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  • 學位論文

網路評論語彙對租屋意願影響之研究

A study of online review sentiments on rental decision

指導教授 : 項衛中

摘要


由於近年來行動應用程式的開發越來越簡單和方便,許多開發人力投入具備高移動性、便利性及位置化的研究,但是在行動應用中提供個人決策輔助功能卻較為少有。現今的租屋模式有別於以往,在個人決策方面,單憑藉房東所刊登的房屋資訊無法完全提供房客得到想要的資訊,例如常見的房屋漏水問題、房間隔音問題、設備毀壞修繕問題及房東為人問題等,往往都是在簽約入住後才發現。這些房屋資訊都必須透過曾經住過該房屋的房客所留下的評論得知,因此在行動應用中提供完整的租屋評論系統顯得格外重要。本研究認為提供經過整理的評論和更方便的認知方式,可以減少房客在作出判斷和決策上所花費的時間與精力,因此提出運用感性工學的概念探討網路評論語彙對租屋意願之影響。 首先,本研究透過訪談與問卷調查挑選出代表性樣本與感性形容詞語彙組,再透過語意差異權重建構評論評分方式,最後藉由相關分析驗證顯示具感性語彙評論對於房客租屋意願具有顯著影響,並且評分的高低對於租屋決策具有高度的相關。本研究發現『房東人很好』及『治安良好』為最具正面影響力的語彙;『房東人很差』、『治安很差』及『網路很不穩』為最具負面影響力的語彙。本研究也運用迴歸分析建立線性模式,透過使用者的網路評論評分來預測該屋的出租率。

並列摘要


The development of mobile application became more popular and convenient in recent years, and many developers worked on topics related to convenience, localization and high mobility. However, the study of providing individual decision support in the mobile application is not very popular. For example, finding a suitable rental house is different to the past. In terms of decision making, it is hard for a renter to choose the based on the house information posted by the landlord. After signing the contract or moving into the house, the tenant could find some problems, such as water leaking, noise and the attitude of the landlord. This house information could be disclosed by the previous tenant reviews. Therefore, in the mobile application, providing an online review system for renting a house is important. More complete and analyzed reviews can help the tenant to make right decisions. This study proposed to apply the concept of Kansei Engineering to investigate the effect of online reviews on the rental decision making. Typical samples and Kansei adjective words were chosen through the interview and questionnaire survey primarily. Based on differences of word meanings, a weighting model for review samples was built. From the results of correlation analysis, the Kansei adjective review has significant effect on the rental willingness, and the grading results are highly correlated with the rental decision. The adjective words with the most positive effects are ‘‘nice landlord’’ and ‘‘secure and safe’’. On the other hand, the adjective words of the most negative effects are ‘‘bad landlord’’, ‘‘insecure’’ and ‘‘bad internet connection’’. A linear regression model was developed to predict the rental rate of a house with the review grading which was assessed by the weighting model.

參考文獻


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