GPS接收器已成為行動裝置的基本內建配備，在過去的幾年裡，許多行動軟體開發者以這項配備為基礎，推出了他們自己的產品以提供導航服務。如今，導航行動軟體已成為許多行動用戶生活中不可或缺的工具。透過導航行動軟體，使用者可以很快地找到路徑以前往其想要到達的地方，途程中並且可以享受推薦的服務及拜訪有趣的景點。導航類行動軟體的蓬勃發展雖然帶給了消費者多元化的選擇，但也使開發者之間的競爭不斷加劇。為了強化自身產品及服務的競爭力，開發者因此需要花費許多精力在提高使用者的體驗品質，一方面用以增加使用者的忠誠度，另一方面吸引更多的使用者使用。線上評論是現今許多使用者在決定是否購買特定軟體時會參考的資訊，這些評論來自使用者體驗過特定軟體後的心得，具有相當程度的影響力，也是開發者改善自身產品的參考依據。線上評論數量龐大且非結構化，因此透過閱讀以萃取重要資訊相當困難。本研究對導航類行動軟體收集Apple's App Store Taiwan的線上評論，以意見分析與深度學習法從評論中推導出最顯著影響使用者體驗滿意度的30個特徵品質，據以建立導航類行動軟體的體驗品質評估模型。本研究並以adjusted R2 (調整後決定係數) 、RMSE(均方根誤差)及MAPE(平均絕對百分比誤差)對該模型的自變數的解釋力及預測能力進行評估。評估結果顯示adjusted R2、RMSE及MAPE各為0.9739、0.29及5.647%，此結果優於早先鄭育儒(2015)等所使用的逐步線性回歸分析。本研究所建立的模型能應用以提供消費者從中了解特定導航軟體的品質性能以進行下載決策，也能使開發者確認其產品及服務的改進方向。本論文最後將對研究成果進行討論，並提出建議。

GPS receivers have become the basic built-in feature of smartphones. With the feature, numerous app developers launched their apps over the past years to provide navigation services. Consequently, navigation apps have been mobile users’ indispensable tool for finding their ways to destinations, enjoying recommended services or visiting interesting spots. The rapid growth of navigation apps has been diversifying consumer choices, as a result, the app developers’ competition has been intensified. Developers thus spend considerable efforts to enhance users’ Quality of Experience (QoE) so as to maintain users’ loyalty and to attract more consumers. Writing by experienced users, online reviews are the critical information resources that influence a user’s app purchase decision. Moreover, the online reviews recommend the improvement suggestions to the developers. The online reviews are unstructured, uneven in quality and massive in quantity, therefore, it is difficult to extract valuable information from them. We collected online reviews of Navigation app form Apple's App Store Taiwan, used opinion analysis and Deep Learning method to derive 30 most influential quality features. Accordingly, this study developed a quality of experience assessment and prediction model for Navigation Application. We tested the explanatory and predictive powers of the model using adjusted R-squared (R2, the coefficient of determination), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE), which were 0.9739 and 0.29 and 5.647% in order. Furthermore, a comparison between the performance of Deep Learning and the Stepwise Linear Regression used by Zheng (2015) shows that the former outperformed the latter. With the QoE model, practical applications demonstrated how users and developers can benefit from it to respectively make download decisions and identify apps’ improvement directions.

1. 李易霖. (2014). 萃取線上評論品質特徵以發展行動軟體搜尋法. (碩士), 國立屏東科技大學. 屏東縣.
2. 江明晏. (2015). 蘋果App Store下載數 突破1000億次. Retrieved November 25, 2015, from https://tw.news.yahoo.com/%E8%98%8B%E6%9E%9Capp-store%E4%B8%8B%E8%BC%89%E6%95%B8-%E7%AA%81%E7%A0%B41000%E5%84%84%E6%AC%A1-032935792--finance.html
3. 吳明隆. (2003). SPSS 統計應用學習實務—問卷分析與應用統計, 台北: 知城.
4. 呂怡萱. (2010). 運用 GPS 分析市區公車站位使用頻率及因素研究.
5. 周欣穎. (2012). 台灣旅遊消費結構與滿意度之研究. (碩士), 真理大學, 新北市.
6. 邱仲麟. (2014). SaaS 雲端服務軟體品質測試之研究.
7. 洪雅雯. (2008). 台灣上市化學生技醫療類股價指數預測之研究. (碩士), 國立成功大學, 台南市.
8. 張靖. (2009). 應用衛星定位系統資料庫分析駕駛路線選擇行為之研究.
9. 張靖. (2010). 發展具有學習駕駛路線選擇偏好功能之車輛導航系統.
10. 陳順宇. (2000). 迴歸分析, 台北: 華泰書局.
11. 戴彬. (2015). 台灣市場小嗎？行動平台營收高居亞洲第四、全球第十. Retrieved January 26, 2015, from http://www.inside.com.tw/2015/01/26/app-annie-mobile-market-trend
12. 蘇文彬. (2012). Gartner：2015年以前行動軟體開發將超越PC. Retrieved January 16, 2012, from http://www.ithome.com.tw/node/71777
13. 蘇文彬. (2014). 愛立信：2015年行動使用者數將超越全球總人口. from http://www.ithome.com.tw/news/89472
14. 賴宥霖. (2017). 以線上評論量測旅遊行動軟體的使用者體驗品質. (碩士), 國立屏東科技大學, 屏東縣.
15. 鄭育儒. (2015). 萃取線上評論重要意見以建立娛樂類行動軟體的服務體驗品質評估模型. (碩士), 國立屏東科技大學, 屏東縣.
16. 吳璟宜. (2016)。 從線上評論分析社交類行動應用軟體之品質特徵. (碩士), 國立高雄師範大學, 高雄市。
17. 溫宗憲. (2013). 具備語音功能的雲端應用: 個人化語言模型與互動式語音文件檢索. 臺灣大學電機工程學研究所學位論文, 1-98.
18. 葉怡成. (2006). 類神經網路模式應用與實作, 儒林圖書公司, 台北.
19. 蔡鑫, 娄京生. (2018). 基于 LSTM 深度学习模型的中国电信官方微博用户情绪分析. 电信科学, 33(12), 136-141.
20. 蔡佳伶. (2017). 2017全球手機用戶估破50億 亞洲最驚人. Retrieved February 28, 2017, from https://www.chinatimes.com/realtimenews/20170228003909-260412
21. 何佩珊. (2017). Google Play五周年，關鍵數據大公開. Retrieved March 21, 2017, from https://www.bnext.com.tw/article/43672/key-number-of-google-play-in-last-five-years
22. 陳曉莉. (2018). App Annie：台灣在App Store上的消費力成長居全球第二. Retrieved January 26, 2018, from https://www.ithome.com.tw/news/120951
23. Bunny Lau. (2018). 手機APP下載量排行榜出爐!10年來Top 10的Apps是哪些. Retrieved July 5, 2018, from https://popbee.com/lifestyle/gadgets/apple-ios-app-store-top10-apps-all-time
24. 李宏毅. (2016). 專題-人工智慧與 AlphaGo 什麼是深度學習. 數理人文(10).
25. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., . . . Isard, M. (2016). Tensorflow: a system for large-scale machine learning. Paper presented at the OSDI.
26. Agboma, F., & Liotta, A. (2012). Quality of experience management in mobile content delivery systems. Telecommunication Systems, 49(1), 85-98.
27. AlMahmud, A., Mubin, O., & Shahid, S. (2009). User experience with in-car GPS navigation systems: comparing the young and elderly drivers. Paper presented at the Proceedings of the 11th International Conference on Hum
28. Aralikatte, R., Sridhara, G., Gantayat, N., & Mani, S. (2018). Fault in your stars: an analysis of Android app reviews. Paper presented at the Proceedings of the ACM India Joint International Conference on Data Science
29. Bohnenberger, T., Jameson, A., Krüger, A., & Butz, A. (2002). Location-aware shopping assistance: Evaluation of a decision-theoretic approach Human Computer Interaction with Mobile Devices (pp. 155-169): Springer.
30. Brown, M., Houghton, R., Sharples, S., & Morley, J. (2015). The attribution of success when using navigation aids. Ergonomics, 58(3), 426-433.
31. Chang, I.-C., Tai, H.-T., Yeh, F.-H., Hsieh, D.-L., & Chang, S.-H. (2013). A VANET-based route planning algorithm for travelling time-and energy-efficient gps navigation app. International Journal of Distributed Sensor
32. Chen, N., Lin, J., Hoi, S. C., Xiao, X., & Zhang, B. (2014). AR-Miner: mining informative reviews for developers from mobile app marketplace. Paper presented at the Proceedings of the 36th International Conference on S
33. Chen, Y., Fay, S., & Wang, Q. (2011). The role of marketing in social media: How online consumer reviews evolve. Journal of Interactive Marketing, 25(2), 85-94.
34. Cheng, Y.-H., & Ho, H.-Y. (2015). Social influence's impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883-887.
35. Chevalier, J. A., & Mayzlin, D. (2006). The effect of word of mouth on sales: Online book reviews. Journal of marketing research, 43(3), 345-354.
36. Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function. Mathematics of control, signals and systems, 2(4), 303-314.
37. Dai, Q. (2011). A survey of quality of experience Energy-Aware Communications (pp. 146-156): Springer.
38. DeVleeschauwer, B., Van de Meerssche, W., Simoens, P., De Turck, F., Dhoedt, B., Demeester, P., . . . Van Caenegem, T. (2006). On the enhancement of QoE for IPTV services through knowledge plane deployment. Broadband
39. Gong, Y., Yang, F., Huang, L., & Su, S. (2009). Model-based approach to measuring quality of experience. Paper presented at the Emerging Network Intelligence, 2009 First International Conference on.
40. Guzman, E., & Maalej, W. (2014). How do users like this feature? a fine grained sentiment analysis of app reviews. Paper presented at the Requirements Engineering Conference (RE), 2014 IEEE 22nd International.
41. Harman, M., Jia, Y., & Zhang, Y. (2012). App store mining and analysis: MSR for app stores. Paper presented at the Proceedings of the 9th IEEE Working Conference on Mining Software Repositories.
42. Hearn, D., & Baker, M. P. (1986). Computer Graphics Prentice Hall International.
43. Hertz, J., Krogh, A., & Palmer, R. G. (1991). Introduction to the theory of neural computation: Addison-Wesley/Addison Wesley Longman.
44. Hoßfeld, T., Schatz, R., Biersack, E., & Plissonneau, L. (2013). Internet video delivery in YouTube: from traffic measurements to quality of experience Data Traffic Monitoring and Analysis (pp. 264-301): Springer.
45. Huval, B., Wang, T., Tandon, S., Kiske, J., Song, W., Pazhayampallil, J., . . . Cheng-Yue, R. (2015). An empirical evaluation of deep learning on highway driving. arXiv preprint arXiv:1504.01716.
46. Iacob, C., Harrison, R., & Faily, S. (2013). Online reviews as first class artifacts in mobile app development. Paper presented at the International Conference on Mobile Computing, Applications,
47. ISO. (2001). ISO/IEC 9126-1:2001 Software Engiering-Product Quality-Part 1 Quality Model. ISO.
48. ISO. (2011). ISO/IEC 25010:2011 - Systems and software engineering - Systems and software Quality Requirements and Evaluation (SQuaRE) - System and software quality models. ISO.
49. Jacob, R., Mooney, P., Corcoran, P., & Winstanley, A. C. (2011). Integrating haptic feedback to pedestrian navigation applications.
50. Jain, M., Rahul, R. C., & Tolety, S. (2013). A study on Indoor navigation techniques using smartphones. Paper presented at the Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conferen
51. Joskowicz, J., & Ardao, J. (2010). A general parametric model for perceptual video quality estimation. Paper presented at the Communications Quality and Reliability (CQR), 2010 IEEE International Workshop Technical Com
52. Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Paper presented at the Advances in neural information processing systems.
53. Kucuktunc, O., Cambazoglu, B. B., Weber, I., & Ferhatosmanoglu, H. (2012). A large-scale sentiment analysis for Yahoo! answers. Paper presented at the Proceedings of the fifth ACM international conference on Web search
54. Laghari, K. U. R., & Connelly, K. (2012). Toward total quality of experience: A QoE model in a communication ecosystem. Communications Magazine, IEEE, 50(4), 58-65.
55. Le, Q. V. (2013). Building high-level features using large scale unsupervised learning. Paper presented at the Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on.
56. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436.
57. Liu, B. (2015). Sentiment analysis: Mining opinions, sentiments, and emotions: Cambridge University Press.
58. Lopez, D., Gonzalez, F., Bellido, L., & Alonso, A. (2006). Adaptive multimedia streaming over IP based on customer oriented metrics. Paper presented at the Computer Networks, 2006 International Symposium on.
59. Matulin, M., & Mrvelj, Š. (2013). State-of-the-practice in evaluation of quality of experience in real-life environments. PROMET-Traffic&Transportation, 25(3), 255-263.
60. Ma, W.-Y., & Chen, K.-J. (2003). Introduction to CKIP Chinese Word Segmentation System for the First International Chinese Word Segmentation Bakeoff. Paper presented at the Proceedings of the second SIGHAN workshop on
61. Min, K., An, K., Jang, I., & Jin, S. (2011). A system framework for map air update navigation service. ETRI Journal, 33(4), 476-486.
62. Möller, S., Engelbrecht, K.-P., Kühnel, C., Wechsung, I., & Weiss, B. (2009). A taxonomy of quality of service and quality of experience of multimodal human-machine interaction. Paper presented at the Quality of Mult
63. Nayebi, M., Cho, H., Farrahi, H., & Ruhe, G. (2017). App store mining is not enough. Paper presented at the Software Engineering Companion (ICSE-C), 2017 IEEE/ACM 39th International Conference on
64. Nevid, J. S., & Rathus, S. A. (2009). Psychology and the Challenges of Life: John Wiley & Sons.
65. Nokia. (2005). Quality of Experience (Quote) of mobile services: Can it be measured and improved.
66. Nowlan, S. J., & Hinton, G. E. (1992). Simplifying neural networks by soft weight-sharing. Neural computation, 4(4), 473-493.
67. Oh, J., Kim, D., Lee, U., Lee, J.-G., & Song, J. (2013). Facilitating developer-user interactions with mobile app review digests. Paper presented at the CHI'13 Extended Abstracts on Human Factors in Computing Systems.
68. Oulasvirta, A., Tamminen, S., Roto, V., & Kuorelahti, J. (2005). Interaction in 4-second bursts: the fragmented nature of attentional resources in mobile HCI. Paper presented at the Proceedings of the SIGCHI conference
69. Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C., Canfora, G., & Gall, H. (2015). How can i improve my app? classifying user reviews for software maintenance and evolution. Paper presented at the Proceedings of t
70. Payán-Serrano, O., Bojórquez, E., Bojórquez, J., Chávez, R., Reyes-Salazar, A., Barraza, M., . . . Corona, E. (2017). Prediction of Maximum Story Drift of MDOF Structures under Simulated Wind Loads Using Artificial Neural Networks. Applied Sciences, 7(6), 563.
71. Perkis, A., Munkeby, S., & Hillestad, O. I. (2006). A model for measuring quality of experience. Paper presented at the Signal Processing Symposium, 2006. NORSIG 2006. Proceedings of the 7th Nordic.
72. Perez, S. (2014). iTunes app store now has 1.2 million apps, has seen 75 billion downloads to date. Retrieved on August, 15, 2014.
73. Pielot, M., Poppinga, B., Heuten, W., & Boll, S. (2012). PocketNavigator: studying tactile navigation systems in-situ. Paper presented at the Proceedings of the SIGCHI Conference on Human Factors in Computing Systems.
74. Rec, I. (2008). P. 10: Vocabulary for performance and quality of service, Amendment 2: New definitions for inclusion in Recommendation ITU-T P. 10/G. 100. Int. Telecomm. Union, Geneva.
75. Reiter, U., Brunnström, K., De Moor, K., Larabi, M.-C., Pereira, M., Pinheiro, A., . . . Zgank, A. (2014). Factors influencing quality of experience Quality of Experience (pp. 55-72): Springer.
76. Steel, R. G. D., & Torrie, J. H. (1960). Principles and procedures of statistics: with special reference to the biological sciences. McGraw-Hill.
77. Stellwagen, E. (2016). Forecasting 101: a guide to forecast error measurement statistics and how to use them. Retrieved July, 12.
78. ROVELO RUIZ, G., Abat, M., & Camahort, E. (2015). Studying the User Experience with a Multimodal Pedestrian Navigation Assistant.
79. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985). Learning internal representations by error propagation. Retrieved from
80. Sahara, S., & Wahyudi, M. (2010). K-NEAREST NEIGHBORS SEBAGAI ANALISIS SENTIMENT REVIEW PRODUK APPSTORE FOR ANDROID.
81. Soldani, D., Li, M., & Cuny, R. (2007). QoS and QoE management in UMTS cellular systems: John Wiley & Sons.
82. Timmermann, J., Poppinga, B., Pielot, M., Heuten, W., & Boll, S. (2013). TimedNavigation: a time-based navigation approach. Paper presented at the Proceedings of the 15th international conference on Human-computer inte
83. Ullah, I., & Petrosino, A. (2016). About pyramid structure in convolutional neural networks. Paper presented at the Neural Networks (IJCNN), 2016 International Joint Conference on.
84. Vinodhini, G., & Chandrasekaran, R. (2012). Sentiment analysis and opinion mining: a survey. International Journal, 2(6).
85. Wang, L., Groves, P. D., & Ziebart, M. K. (2013). Urban positioning on a smartphone: Real-time shadow matching using gnss and 3d city modelsAbadi, M., Barham, P., Chen, J., Chen, Z., Davis, A.,
86. Wang, Y.-Y., Lin, H.-H., Wang, Y.-S., Shih, Y.-W., & Wang, S.-T. (2018). What drives users’ intentions to purchase a GPS Navigation app: The moderating role of perceived availability of free substitutes. Internet Research, 28(1), 251-274.
87. Xi, Y., Schwiebert, L., & Shi, W. (2014). Privacy preserving shortest path routing with an application to navigation. Pervasive and Mobile Computing, 13, 142-149.