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    請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/105533

    題名: 網路情感分析對於手機應用程式評價之影響的研究
    其他題名: A study of the impact of the internet sentiment analysis on APP rating
    作者: 酆偉寬;Fong, Wei-Kuan
    貢獻者: 淡江大學資訊管理學系碩士班
    蕭瑞祥;Shaw, Ruey-Shiang
    關鍵詞: 意見探勘;情感分析;SVM;Opinion Mining;Sentiment analysis
    日期: 2015
    上傳時間: 2016-01-22 14:58:28 (UTC+8)
    摘要: 根據Surikate 與GfK調查 (2013)顯示,85%使用者會在下載應用程式(Application, APP)前,參考在APP STORE(iOS Application Store, APP STORE)上其它用戶對該APP的評價,這些參考項目包含該APP的預覽圖、銷售價格與內文評論等,可知使用者通常會參考其它用戶的使用經驗以決定是否下載該應用程式。
    根據資策會FIND調查我國智慧型行動裝置持有人數於2014年底已達1400萬以上,又依據LINE的官方統計,在我國LINE的下載次數於2014年6月中突破1700萬大關,下載次數冠居所有APP;從數據上來看,LINE在我國行動族群間之普及率早已超過九成,但該軟體在APP STORE中的用戶評分卻僅有2.4分,足見用戶評分與下載次數並無決定性的相互影響關係,且難以反應出真實的評價水準。
    為了驗證本研究提出之評分模型效能,本研究亦將透過系統發展研究法,以支持向量機(Support Vector Machine, SVM)分類模型將網路評論文本依特徵項轉化為特徵量文本後,進行訓練與測試,再將支持向量機分類模型產出之分類結果比對人工判讀之結果,計算其準確率做為系統驗證的依據。
    According to Surikate and GfK (2013), 85 percent Application (APP) users refer to the comments and ratings of other users on iOS Application Store, APP STORE before actually downloading the APP. It can be noticed that people commonly examine the user experience of others when determining whether to download the product.
    The investigations conducted by the FIND (2014) states that people who possess a smartphone in our nation has risen over 14 million. The statistics from the LINE official website (June, 2014) also indicates that its downloads had made a significant breakthrough of over 17 million, making LINE the most downloaded APP. From this information, we can conclude that LINE has a penetration rate of over 90 percent among Taiwanese cellphone users. However, this APP is rated a 2.4 in the APP STORE, implying that user ratings does not determine the amount of downloads. Moreover, this score limitedly reflects the real value of the product.
    Based from this observation, the objective of this research is to discuss the authentic ratings of cellphone APP users. Via adapting a scientific method, the collection of APP word ratings on the internet will be converted into quantized data. Analysis will be done with the assistance of manual interpretations in order to propose a modal that adequately reflects the user experience.
    For the purpose of verifying the efficiency of this rating model, this study is built on the System Development Methodology. After the wording of internet comments are transformed into characterized quantized data, it is then entered into the classification of the Support Vector Machine (SVM) for further training and testing. The data that is classified by the SVM is compared with the outcome of manual interpretations so that to calculate the accuracy rate.
    The results of this experiment indicate that through corpus categorizing, this modal reached an accuracy rate of 90 percent. This demonstrates that this APP rating modal has outstanding reflection ability. Thereby, may the results of this research contribute to future studies in this field.
    顯示於類別:[資訊管理學系暨研究所] 學位論文


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