網路科技日新月異漸漸地改變了民眾日常的生活習慣。例如:過去坐在電視機前面觀賞電視節目是民眾習以為常的休閒娛樂，近年來隨著電視盒產業及網路媒體娛樂迅速發展，民眾只需透過OTT電視盒加上一條網路線，甚至無需支付額外費用就能享受多樣化的平台服務，也不再需要電視機才能觀看節目或追劇。換言之，OTT的誕生改變了民眾觀賞頻道型態，無論在任何時間或任何地點，只要有網路的地方再加上電視機、手機、平板、電腦等平台設備或OTT電視盒，就能透過安裝APP來連結觀看。然而OTT電視盒品牌在市面上琳瑯滿目，為分析電視盒在消費性市場評價及口碑，選擇最合適的策略產品，行銷人員經常透過電話做行銷問卷調查收集意見或利用網路搜尋相關資訊。
   由於論壇社群意見足以影響消費者的行為模式，甚至超越媒體，已有相當多單位也利用意見探勘系統來蒐集論壇文章，分析各面向的口碑及評價。本研究將針對各品牌的電視盒進行口碑分析，經由中文意見探勘系統搜尋和分析論壇中與電視盒相關的文章及產生各品牌電視盒正負評論的完整句，再經由報表呈現同一期間內不同品牌電視盒被討論的文章數及有正負評價筆數，做為公司行銷人員分析各電視盒品牌的網路口碑，提供公司經營策略參考。

With the rapid development of the TV box industry and online media entertainment, the birth of OTT has changed the way people watch channels, regardless of the time or place, as long as there is a network, plus TV, mobile phone, tablet, computer, etc. Platform devices or OTT TV boxes can be viewed through the installation of the app. However, the OTT TV box brand is very popular in the market. Because the company currently has a small number of users, in order to analyze the evaluation and reputation of the TV box in the consumer market and select the most suitable strategy products, the marketing staff often conducts marketing questionnaires through the telephone to collect opinions or Use the web to find relevant information.
    This study will conduct a word-of-mouth analysis for TV boxes of various brands, search and analyze the articles related to TV boxes in the forum and the complete sentences that generate positive and negative comments of each brand TV box through the Chinese opinion mining system, and then present different brands in the same period through the report. The number of articles discussed in the TV box and the number of positive and negative evaluation articles, as the company's marketing staff to analyze the Internet word of mouth of each TV box brand, provide a reference for the company's business strategy.

目錄
第1章 緒論1
1-1 研究動機與目的1
1-2 研究架構4
第二章 文獻探討5
2-1 OTT電視盒演化史5
2-2意見元素定義8
2-3中文意見探勘系統相關研究10
2-4本研究室開發的中文意見探勘系統簡介12
第三章 研究方法15
3-1問題陳述15
3-2設定篩選關鍵字及排除關鍵字21
3-3 論壇評價分析25
第4章 實例分析32
4-1 論壇頻道來源32
4-2 實例分析35
第5 章 結論43
參考文獻	44
附錄A 英文論文48

 
圖目錄
圖2-1 網路電視機上盒電視棒及智慧型電視使用狀況5
圖2-2 家中擁有智慧電視  Base:(N=1074)7
圖2-3 家中智慧電視連接寬頻網路  Base:(N=207)7
圖2-4 系統架構12
圖2-5 文章下載與配置流程圖13
圖2-6 中文意見探勘系統文章分析流程圖14
圖3-1 MOD事件討論聲量16
圖3-2 論壇專門版圖示(PTT MOD版)17
圖3-3 論壇專門版圖示(PTT MOD版)18
圖3-4 論壇其他版圖示(PTT MobilComm版)19
圖3-5 論壇關鍵字搜尋演算法22
圖3-6 文章與完整句29
圖4-1 各品牌電視盒文章數及比例35
圖4-2 各品牌電視盒文章數及正負評筆數36
圖4-3 安博文章分析列表37
圖4-4 小米盒子文章分析列表38
圖4-5 鴻海便當討論文章數及正負評筆數39
圖4-6 鴻海便當文章與完整句40
圖4-7 雷爵機上盒討論文章數及正負評筆數41
圖4-8 雷爵機上盒(rocktek)文章列表	42

 
表目錄
表2-1 意見元素定義表9
表3-1 關鍵字及討論標題對照表24
表3-2 關鍵字及討論標題對照表24
表3-3 電視盒及其相關同義字25
表3-4 公司名稱及其關鍵字與同義詞對照表28
表3-5 MOD電視盒正負評統計表31
表4-1 論壇頻道來源33
表4-2 電視盒總文章數及正負評筆數34

[1] Addiction Technology / Netflix set up a Taiwan office in Hsinchu to strengthen its cooperation with local content (2016.06.07) Taiwan OTT TV usage behavior survey (2016/11)
[2] Inter-Market Market Research Consultant (2007)
[3] Huang J H & Chen, Y.F. (2006) Herding in online product choice. Psychology and marketing, 23(5), 413-428
[4] Chen Yingzhen (2008), Taiwan Lotte Station - expected to reach 1,000 at the end, advertising magazine, (205), 22-23
[5] Thorsten, H.T., & Gianfranco, W. (2003). Electronic word mouth: Moyives for and consequences of reading customer articulationon the internet. International Journal of Electronic Commerce, 8(2), 51-74.
[6] Inter-Market Market Research Consultant (2018.12)
[7] National Communications and Communication Committee "Type Certification Enquiry Page" online information (https://nccmember.ncc.gov.tw/Application/Fun/Fun016.aspx)
[8] Summary report of 106 years of radio and television market survey results, (2018)
[9] Liu Zhenhui, “Design and Implementation of Daily Reports Applied to Chinese Opinion Mining System,” Master's thesis of Department of Information Engineering, Department of Information Engineering, Tamkang University, (2018.) 
[10] P. D. Turney, "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews," presented at the Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, Philadelphia, Pennsylvania, 2002.
[11] M. Hu and B. Liu, "Mining and summarizing customer reviews," presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, Seattle, WA, USA, 2004.
[12] L.-W. Ku and H.-H. Chen, "Mining opinions from the Web: Beyond relevance retrieval," Journal of the American Society for Information Science and Technology, vol. 58, pp. 1838-1850, 2007.
[13] B. Liu and L. Zhang, "A Survey of Opinion Mining and Sentiment Analysis Mining Text Data," C. C. Aggarwal and C. Zhai, Eds., ed: Springer US, 2012, pp. 415-463
[14] Z. Li, M. Zhang, S. Ma, B. Zhou, and Y. Sun, "Automatic Extraction for Product Feature Words from Comments on the Web Information Retrieval Technology." vol. 5839, G. Lee, D. Song, C.-Y. Lin, A. Aizawa, K. Kuriyama, M. Yoshioka, et al., Eds., ed: Springer Berlin / Heidelberg, 2009, pp. 112-123.
[15] M. Chen and T. Yao, "Combining dependency parsing with shallow semantic analysis for Chinese opinion-element relation identification," in Universal Communication Symposium (IUCS), 2010 4th International (pp. 299-305), 2010, pp. 299-305.
[16] P. Ting-Chun and S. Chia-Chun, "Using Chinese part-of-speech patterns for sentiment phrase identification and opinion extraction in user generated reviews," in Digital Information Management (ICDIM), 2010 Fifth International Conference on (pp. 120-127), 2010, pp. 120-127.
[17] V. Hatzivassiloglou and K. R. McKeown, "Predicting the semantic orientation of adjectives," 1997, pp. 174-181.
[18] G. Qiu, B. Liu, J. Bu, and C. Chen, "Expanding domain sentiment lexicon through double propagation," presented at the Proceedings of the 21st international jont conference on Artifical intelligence (pp.1199-1204), Pasadena, California, USA, 2009.
[19] M. Hu and B. Liu, "Mining and summarizing customer reviews," presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177), Seattle, WA, USA, 2004.
[20] S.-M. Kim and E. Hovy, "Determining the sentiment of opinions," presented at the Proceedings of the 20th international conference on Computational Linguistics (pp. 1367), Geneva, Switzerland, 2004.
[21] S. Bin and C. Kuiyu, "Mining Chinese Reviews," in Data Mining Workshops, 2006. ICDM Workshops 2006. Sixth IEEE International Conference on (pp. 585-589), 2006, pp. 585-589.
[22] Y. Qiang, S. Wen, and L. Yijun, "Sentiment Classification for Movie Reviews in Chinese by Improved Semantic Oriented Approach," in System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on (pp. 53b-53b), 2006, pp. 53b-53b.
[23] L. Zhuang, F. Jing, and X.-Y. Zhu, "Movie review mining and summarization," presented at the Proceedings of the 15th ACM international conference on Information and knowledge management, Arlington, Virginia, USA, 2006.
[24] X. Ding, B. Liu, and P. S. Yu, "A holistic lexicon-based approach to opinion mining," presented at the Proceedings of the 2008 International Conference on Web Search and Data Mining (pp. 231-240), Palo Alto, California, USA, 2008.
[25] L. W. Ku, I. C. Liu, C. Y. Lee, K. Chen, and H. H. Chen, "Sentence-Level Opinion Analysis by CopeOpi in NTCIR-7," in Proceedings of the 7th NTCIR Workshop Meeting on Evaluation of Information Access Technologies: Information Retrieval, Question Answering, and Cross-Lingual Information Access (pp. 260-267), Tokyo, Japan, 2008, pp. 260-267.
[26] M. Hu and B. Liu, "Mining and summarizing customer reviews," presented at the Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168-177), Seattle, WA, USA, 2004.
[27] C. Zhang, D. Zeng, J. Li, F.-Y. Wang, and W. Zuo, "Sentiment analysis of Chinese documents: From sentence to document level," J. Am. Soc. Inf. Sci. Technol., vol. 60, pp. 2474-2487, 2009.
[28] L.-W. Ku, H.-W. Ho, and H.-H. Chen, "Opinion mining and relationship discovery using CopeOpi opinion analysis system," Journal of the American Society for Information Science and Technology, vol. 60, pp. 1486-1503, 2009.
[29] Pei-Yi Chiang, “Chinese Opinion Mining System Research on keyword search optimization,” Master's thesis of Department of Information Engineering, Department of Information Engineering, Tamkang University, (2018.)