AppCAT: 基於手機應用程式評論情感分析和產品功能發掘 – 以 iOS 為例

Abstract

使用者對於手機應用程式的評論經常包含抱怨或是建議，此對於手機開發者用於改善使用者經驗和提升滿意度是很有幫助的。然而由於這些評論的品質和當中的雜訊使得手動去分析這些評論並且得到有價值的數據是困難的。\n針對此問題，我們提出了AppCAT，這是一個自動化的評論分析系統，能夠達成App產品功能辨識以及評論的情感分析。AppCAT事先定義了這些產品功能的相關主題字。並且使用相似字技術來延伸這些初始的關鍵字來找到App相關的產品功能。\n除此之外AppCAT能發現這些評論的評論主體(產品功能)並且偵測評論的情感，找出使用者對於該App對應功能的意見。AppCAT使用這些資料並繪製呈雙向柱狀圖以視覺化這些情感量，提供給使用者決定他們是否應該下載這個App。\n對於開發者，他們也可以利用這個系統來知道使用者對於App的大致意見，來做為下個版本改善的依據。

User reviews of mobile apps often contain complaints or suggestions which are valuable for app developers to improve user experience and satisfaction. However, due to the large volume and noisy-nature of those reviews, manually analyzing them for useful opinions is quite challenging. To address this problem, we propose Ap- pCAT, a sentiment and feature mining framework for automated review analysis. AppCAT defines the initial sets of keywords of those comments. And it use word similarity technique to expand the initial sets by grouping other keywords to find out the product features of those apps. Furthermore, AppCAT detects the sentiment and its subject(a product feature) of those reviews and figure out the user attitude towards those product feature of a specific app. AppCAT use those data to plot a bar chart to visualize those feature polarities for users to facilitate if they should consider this app. For the app developers, they can use this system to get the opinion overview of users as a basis of revision.