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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/101078


    请使用永久网址来引用或连结此文件: http://nccur.lib.nccu.edu.tw/handle/140.119/101078


    题名: 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
    AppCAT: Systematic Sentiment Analysis of Mobile Application Reviews
    作者: 黃書韋
    Huang, Shu Wei
    贡献者: 郁方
    Yu, Fang
    黃書韋
    Huang, Shu Wei
    关键词: 手機應用程式
    情感分析
    評論
    mobile application
    sentiment analysis
    reviews
    日期: 2016
    上传时间: 2016-09-01 23:46:01 (UTC+8)
    摘要: 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。
    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。
    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。
    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.
    參考文獻: [1] app-attention-span-research-report-1.pdf. https://www.appdynamics.com/media/ uploaded-files/1425406960/app-attention-span-research-report-1.pdf.(Accessed on 03/03/2016).
    [2] AppBrainStat. http://www.appbrain.com/stats. Accessed: 2015-06-12.
    [3] Gartner Says Worldwide Sales of Cellular-Embedded Mobile PCs, Tablets and Mo- bile Hot Spot Devices Will Exceed 112 Million in 2015. http://www.gartner.com/ newsroom/id/3064718. Accessed: 2015-06-12.
    [4] Github - vacuumv/appcat: Mobile application in ios review semtiment analysis. https://github.com/vacuumv/AppCat. (Visited on 01/27/2016).
    [5] Google play android. https://play.google.com/ store/apps?utm_source=apac_med&utm_medium=hasem&utm_ content=May2516&utm_campaign=Evergreen&pcampaignid= MKT-DR-apac-tw-all-med-hasem-py-Evergreen-May2516-1-BKWS%7c%2EHASEM_ kwid_43700011378110954&gclid=Cj0KEQjwhZm7BRCUyfS6ho2VjOEBEiQAumpGMt367Hff9cpZOE gclsrc=aw.ds#/now. (Accessed on 06/20/2016).
    [6] https://itunes.apple.com/rss/customerreviews/id=340233007/sortby=mostrecent/page=1/json. https://itunes.apple.com/rss/customerreviews/id=340233007/sortby= mostrecent/page=1/json. (Accessed on 06/20/2016).
    [7] Improving hls on android - jw player sdk for android 1.2. https://www.jwplayer. com/blog/improving-hls-android-sdk-1-2/. (Accessed on 03/03/2016).
    [8] itunes apple. http://www.apple.com/tw/itunes/charts/free-apps/. (Accessed on 06/20/2016).
    [9] Product/service features and benefits - entrepreneur- ship.org. http://www.entrepreneurship.org/resource-center/ productservice-features-and-benefits.aspx. (Accessed on 06/20/2016).
    [10] The Statistics Portal. http://www.statista.com/statistics/276623/ number-of-apps-available-in-leading-app-stores. Accessed: 2015-06-12.
    [11] What app users care about when sharing personal data: Permissions – martindale.com. http://www.martindale.com/intellectual-property-law/ article_Mintz-Levin-Cohn-Ferris-Glovsky-Popeo-PC_2220436.htm. (Accessed on 03/03/2016).
    [12] Lillian Lee Bo Pang. Opinion Mining and Sentiment Analysis. Foundations and Trends
    [13] Xing Fang and Justin Zhan. Sentiment analysis using product review data. Journal of Big Data, 2(1):5, 2015.
    [14] Leonard Hoon, Rajesh Vasa, Jean-guy Schneider, and John Grundy. An Analysis of the Mobile App Review Landscape : Trends and Implications. pages 1–23, 2013.
    [15] Leonard Hoon, Rajesh Vasa, Jean-Guy Schneider, and John Grundy. An Analysis of the Mobile App Review Landscape: Trends and Implications. Technical report, Department of Computer Science and Software Engineering, Swinburne University of Technology, July 2013.
    [16] Su Su Htay and Khin Thidar Lynn. Extracting product features and opinion words using pattern knowledge in customer reviews. TheScientificWorldJournal, 2013:394758, 2013.
    [17] Khairullah Khan, Baharum Baharudin, and Aurangzeb Khan. Identifying product features from customer reviews using hybrid patterns. International Arab Journal of Information Technology, 11(3):281–286, 2014.
    [18] Efthymios Kouloumpis, Theresa Wilson, and Johanna Moore. Twitter Sentiment Analysis : The Good the Bad and the OMG ! pages 538–541, 2011.
    [19] Bing Liu. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167, 2012.
    [20] George A. Miller. Wordnet: A lexical database for english. COMMUNICATIONS OF THE ACM, 38:39–41, 1995.
    [21] Subhabrata Mukherjee and Pushpak Bhattacharyya. Feature specific sentiment anal- ysis for product reviews. Computational Linguistics and Intelligent Text Processing, pages 1–12, 2012.
    [22] Rebecca Passonneau. Sentiment Analysis of Twitter Data.
    [23] Ana Maria Popescu and Orena Etzioni. Extracting product features and opinions from reviews. Natural Language Processing and Text Mining, (October):9–28, 2007.
    [24] Ana-Maria Popescu and Orena Etzioni. Extracting Product Features and Opinions from Reviews, pages 9–28. Springer London, London, 2007.
    [25] Phong Minh Vu, Tam The Nguyen, Hung Viet Pham, and Tung Thanh Nguyen. Mining User Opinions in Mobile App Reviews : A Keyword-based Approach. 2015.
    描述: 碩士
    國立政治大學
    資訊管理學系
    103356026
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0103356026
    数据类型: thesis
    显示于类别:[資訊管理學系] 學位論文

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