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    政大機構典藏 > 商學院 > 資訊管理學系 > 學位論文 >  Item 140.119/68235
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/68235

    Title: 對使用者評論之情感分析研究-以Google Play市集為例
    Research into App user opinions with Sentimental Analysis on the Google Play market
    Authors: 林育龍
    Lin, Yu Long
    Contributors: 姜國輝
    Lin, Yu Long
    Keywords: 情感分析
    Sentiment Analysis
    Text Classification
    Support Vector Machine
    Social Network Analysis
    Correspondence Analysis
    Date: 2013
    Issue Date: 2014-08-06 11:41:16 (UTC+8)
    Abstract: 全球智慧型手機的出貨量持續提升,且熱門市集的App下載次數紛紛突破500億次。而在iOS和Android手機App市集中,App的評價和評論對App在市集的排序有很大的影響;對於App開發者而言,透過評論確實可掌握使用者的需求,並在產生抱怨前能快速反應避免危機。然而,每日多達上百篇的評論,透過人力逐篇查看,不止耗費時間,更無法整合性的瞭解使用者的需求與問題。
    While the number of smartphone shipment is continuesly growing, the number of App downloads from the popular app markets has been already over 50 billion. By Apple App Store and Google Play, ratings and reviews play a more important role in influencing app difusion. While app developers can realize users’ needs by app reviews, more than thousands of reviews produced by user everday become difficult to be read and collated.
    Sentiment Analysis researchs encompass supervised and unsupervised methods for analyzing review text. The supervised learning is proven as a useful method and can reach high accuracy, but there are limits where future trend can not be recognized and the labels of individual classes must be made manually.
    We concentrate on two issues, viz Sentiment Orientation and Popular Topic, to propose a Chinese Sentiment Analysis method which combines supervised and unsupervised learning. At First, we use unsupervised learning to label every review articles and produce visualized reports. Secondly, we employee supervised learning to build classification model and verify the result.
    In the experiment, the Chinese WordNet is used to build sentiment lexicon to determin review’s sentiment orientation, but the result shows it is weak to find out negative review opinions. In the Topic Extraction phase, we apply two clustering methods to extract Popular Topic classes and its result is excellent by using of NPMI Model with Social Network Analysis Method i.e. Concor. In the supervised learning phase, the accuracy of Sentiment Orientation class is 87% and the accuracy of Popular Topic class is 96%.
    In this research, we conduct an exemplification of the unsupervised method by means of Chinese WorkNet and Social Network Analysis to determin the review classes. Also, we build a comprehensive visualized report to realize users’ feedbacks and utilize classification to explore new comments. Last but not least, with Chinese Sentiment Analysis of this research, and the competitive intelligence in App market can be provided to the App develops.
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    Description: 碩士
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0101356028
    Data Type: thesis
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