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題名:手持裝置遊戲之使用者評論探勘
作者:宋瑞蛟
作者(外文):Re-Jiau Sung
校院名稱:元智大學
系所名稱:資訊管理學系
指導教授:邱昭彰
學位類別:博士
出版日期:2014
主題關鍵詞:手持裝置文本分析方法情感分類時間序列預測遊戲Google Playhandheld devicetext analytics approachsentiment classificationtime series forecastinggameGoogle Play
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本研究應用自行開發的中文文本分析方法探勘使用者對遊戲軟體各種屬性的評論,實證顯示此中文文本分析方法可以獲得良好的網路文字探勘成果。基於語料庫研究模式進行文件及文句特徵情緒分類,進一步分析使用者對各項遊戲屬性的正反面意見。本研究對文件情緒分類提出監督式基因演算/k-平均值分類法,並與其他分類技術實驗結果進行比較。對於文句特徵情緒分類提出混合監督式語義指向演算法與啟發式短語規則法,找出遊戲使用者對遊戲的整體印象、遊戲性、美術性、音樂性、穩定性及開發廠商的正反面意見,實證結果顯示,該方法可以建立有效的分類成果。本研究進一步分析使用者評論的各項特徵,包含使用者性別、遊戲類別、星級評等、遊戲屬性,以及高人氣遊戲。採用內容分析與對應分析,結果顯示許多有趣的現象,洞悉使用者評論所關注的議題及正負面評價,同時提供開發商最佳的實務分析。另外,星級評等是應用軟體最常見的排行機制,使用者群體對應用軟體的星級評等向為新使用者所信任。本研究針對每日星級評等提出三種混合基因演算時間序列模型進行預測,包含基因演算/自回歸移動平均、基因演算/支援向量迴歸,以及基因演算/自回歸移動平均_支援向量迴歸模型進行預測,實證結果顯示基因演算/自回歸移動平均模型,透過最佳化參數設定,對於星級評等的預測結果優於其它模型。
This research investigates the review analytics of Google Play games using a proposed text analytics approach to extract user sentiments about games expressed in Chinese. Based on a corpus study, document- and feature-level sentiment classification are examined to discover about reviews and which attributes of games users felt good or bad. A supervised GA/k-means for classification approach is proposed for document-level sentiment classification. For feature-level sentiment classification, an approach mixed with supervised semantic orientation algorithms and heuristic n-phrase rule is developed to find out user opinions on game attributes including overall impression of a game and different aspects of gameplay, aesthetic, musicality, stability, and developer. The experimental results show that the proposed approaches can help construct effective classification models with acceptable performance. The user reviews are further analyzed various characteristics such as reviewer’s gender, games categories, star ratings, game attributes, and high sentiment games. The results of content analysis accompanied with correspondence analysis show many interesting facts that can provide in-depth insight into users’ concern as well as best practices for developers or managers. Additionally, the star rating is the most common mechanism for users’ confidence by crowd-sourced app ratings. Three GA-based hybrid time series models, GA/ARIMA, GA/SVR, and GA/ARIMA_SVR, are developed for daily star rating forecasting. The evidences show that GA/ARIMA, linear model with optimized parameters, outperforms other models for star rating forecasting.
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