課程資訊
課程名稱
社群媒體分析
Social Media Analytics 
開課學期
111-2 
授課對象
管理學院  資訊管理學系  
授課教師
魏志平 
課號
IM5060 
課程識別碼
725 U3700 
班次
 
學分
3.0 
全/半年
半年 
必/選修
選修 
上課時間
星期二7,8,9(14:20~17:20) 
上課地點
管二301 
備註
建議先修過程式設計、資料探勘/文字探勘、機器學習與陳建錦合授
限學士班四年級以上
總人數上限:50人 
 
課程簡介影片
 
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課程概述

Since the advent of Web 2.0 and online social networking, social media platforms, establishing and fostering general-purpose or interest-based communities that give their users the power to actively create and contribute user-generated contents and connect and interact with others, have exploded in popularity. As a result, social media has become one unique, novel source of big data, providing great opportunities and magnificent potential for research to analyze and understand human behavioral patterns or to develop advanced analytics techniques that analyze social media data for business decision support or more effective social media management. For example, individuals increasingly share on social media platforms their experiences with, preferences for, and opinions about a wide range of products, entities (e.g., organizations, celebrities, politicians), emerging events, public policies, and so on. Organizations increasingly rely on these user reviews to answer such questions as: What do customers say about us and about our competitors? What do they like or dislike about our products? What causes customers to become dissatisfied? 

課程目標
Social media analytics (SMA) refers to “the process and methods of collecting and analyzing data gathered from social media channels to support business decisions.” The objective of this course is to help students understand commonly discussed topics and their corresponding analytics methods related to social media analytics. This course is structured into three modules: SMA essentials, network-based SMA methods, and text-based SMA methods. Technically speaking, social media analytics is a confluence of research in data mining, text mining, and social network analysis. Thus, in the “SMA essentials” module, we will cover these fundamental building blocks of social media analytics, including 1) data mining essentials, 2) text mining essentials, and 3) social network analysis and link prediction. The “network-based SMA methods” module will cover the topics that heavily rely on the structural analysis of social networks to support the analysis of social media. They include 1) community detection, 2) influence modeling, and 3) opinion leader detection. In the “text- based SMA methods” module, we will discuss the methods and applications that predominantly exploit the texts (user-generated contents) shared on social media platforms. They include 1) sentiment analysis, 2) social media for marketing intelligence, 3) user profiling, and 4) fake review detection. 
課程要求
 
預期每週課後學習時數
 
Office Hours
 
指定閱讀
 
參考書目
 
評量方式
(僅供參考)
   
課程進度
週次
日期
單元主題
第1週
2/21  Introduction to the Course 
第2週
2/28  Holidays (No Class) 
第3週
3/7  Data Mining Essentials 
第4週
3/14  Text Mining Essentials
 
第5週
3/21  Social Network Analysis and Link
Prediction 
第6週
3/28  Community Detection 
第7週
4/4  Holidays (No Class) 
第8週
4/11  Influence Modeling 
第9週
4/18  Opinion Leader Detection 
第10週
4/25  Project Proposal Presentation 
第11週
5/2  Sentiment Analysis (I) 
第12週
5/9  Sentiment Analysis (II) 
第13週
5/16  Social Media for Marketing Intelligence 
第14週
5/23  User Profiling and Fake Review Detection 
第15週
5/30  Examination 
第16週
6/6  Project Presentation