政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/67902

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题名:	社群感測器：社群媒體分析工具之設計 Social Sensor: a Tool for Social Media Analysis
作者:	吳君孝 Wu, Chun Hsiao
贡献者:	李蔡彥 Li, Tsai Yen 吳君孝 Wu, Chun Hsiao
关键词:	社群感測器社群媒體資料科學鉅量資料分析大數據資料分析資料探勘自動化分析系統設計 Social Sensor Social Media Data Science Big Data Analysis Big Data Data Analysis Data Mining Automated Analysis System Design
日期:	2013
上传时间:	2014-07-29 16:11:51 (UTC+8)
摘要:	社群媒體網絡的興起，架構起了龐大且複雜的新形態網路結構，而這些蓬勃興起的社群媒體，及其資料開放政策，帶動了全球社群媒體的資料分析狂潮。本研究剖析了國立政治大學水火計畫團隊的研究方法，將其社群媒體分析流程依序分為以下幾個步驟：事件發生、關鍵字收集、資料儲存與管理、等待收割、資料預處理、資料分析、資料視覺化、結果觀察與闡釋等八項步驟。並以「2012年台灣總統大選」分析個案作為實例說明，進一步將該個案分析流程歸納整理後，發現其普遍存在的問題，包含了，資料分析速度趕不上資料收集速度、分析步驟獨立且破碎、手動化分析居多且多使用人工傳遞做為資料交換模式、分析方法零散、個案多且缺乏管理、專家經驗難保留且不易重現、重啟花費成本高且等待結果時間長等問題。因此，本研究以嶄新的概念提出了一套社群媒體資料分析工具，名為社群感測器（Social Sensor），設計上這是一種將實體感測器概念引入到社群媒體世界的一種創新思維，以可管理性、可模組化、可重用性的三大特色建構本系統。使用上，以觀測個案為中心，分析人員可選擇社群媒體類型，如Twitter，也可以自由的選擇分析資料集，與挑選合適的感測器來進行分析，而透過參數預設樣板可快速套用與保留專家的個案分析經驗，亦可針對觀測個案來進行管理。在本研究中亦將分析方法模組化為語系感測器與文本感測器，其中語系感測器的分析方法為本研究所提出。實驗評估結果顯示，過去沒有現成的語系感測器工具，故模組化後相當好用，文本感測器則是強於時間序列分析以及可支援繁體中文。本系統的有用性評價也相當正面。可模組化、可重用性、可管理性評估結果亦為正面。另外在有助於縮短資料分析時程上被認為是很重要的貢獻，解決了過去社群媒體鉅量資料分析所遇上的難題，且確實可透過本系統獲得分析價值，證實了以感測器概念所設計之系統確實有用。 The rise of social media networks established a new style of network structure, and the policy of opening data led the data analysis of global social media frenzy. This research analyzed the process of National Chengchi University`s team in analyzing social media, which is divided into the following steps: event occur, keyword collection, data storage and management, waiting for harvest, data preprocessing, data analysis, data visualization, observation and interpretation. And we use the case study of "2012 Taiwan presidential election" to illustrate the problems in a typical analysis process such as unmatched speed of data analysis with the speed of data collecting, independent and fragmented analysis steps, labor-intensive manual analysis, manual file exchanges, lack of data and case management tools, difficulty to maintain domain expertise, high restart costs and long waiting time, etc. Therefore, in this research, we propose a new concept for social media analysis called “Social Sensor,” which is an innovative design attempting to transform the concept of a physical sensor in a real world into the world of social media with three design features: manageability, modularity, reusability. The system is a case-centered design that allows analysts to select the type of social media (such as Twitter), the target data sets, and appropriate social sensors for analysis. By adopting parameter templates, one can quickly apply the experience of other experts in the beginning of a new case or even create their own templates. We have also modularized the analysis tools into two social sensors: Language Sensor and Text Sensor. Experimental results show that the Language Sensor is quite easy to use and the Text Sensor’s strength is on the functions of time se-ries analysis and the support for Traditional Chinese. The evaluation result of the system on usefulness, modularity, reusability, and manageability are all very positive. The results also show that this tool can greatly reduce the time needed to perform data analysis, solve the problems encountered in traditional analysis process, and obtain useful results. The experimental results reveal that the concept of social sensor and the proposed system design are shown to be use-ful.
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描述:	碩士國立政治大學資訊科學學系 101971017 102
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