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商業智慧實務 Tamkang University Prac&ces of Business Intelligence 意見探勘與情感分析 (Opinion Mining and Sentiment Analysis) 1022BI08 MI4 Wed, 9,10 (16:10-‐18:00) (B113) Min-Yuh Day 戴敏育 Assistant Professor 專任助理教授 Dept. of Information Management, Tamkang University 淡江大學 資訊管理學系 http://mail. tku.edu.tw/myday/ 2014-‐05-‐07 1
課程大綱 (Syllabus) 週次 (Week) 日期 (Date) 內容 (Subject/Topics) 1 103/02/19 商業智慧導論 (IntroducFon to Business Intelligence) 2 103/02/26 管理決策支援系統與商業智慧 (Management Decision Support System and Business Intelligence) 3 103/03/05 企業績效管理 (Business Performance Management) 4 103/03/12 資料倉儲 (Data Warehousing) 5 103/03/19 商業智慧的資料探勘 (Data Mining for Business Intelligence) 6 103/03/26 商業智慧的資料探勘 (Data Mining for Business Intelligence) 7 103/04/02 教學行政觀摩日 (Off-‐campus study) 8 103/04/09 資料科學與巨量資料分析 (Data Science and Big Data AnalyFcs) 2
課程大綱 (Syllabus) 週次 日期 內容（Subject/Topics） 9 103/04/16 期中報告 (Midterm Project PresentaFon) 10 103/04/23 期中考試週 (Midterm Exam) 11 103/04/30 文字探勘與網路探勘 (Text and Web Mining) 12 103/05/07 意見探勘與情感分析 (Opinion Mining and SenFment Analysis) 13 103/05/14 社會網路分析 (Social Network Analysis) 14 103/05/21 期末報告 (Final Project PresentaFon) 15 103/05/28 畢業考試週 (Final Exam) 3
Outline • Social Word-‐of-‐Mouth • Opinion Mining and SenFment Analysis • Social Media Monitoring/Analysis • Resources of Opinion Mining • Opinion Spam DetecFon 4
Word-‐of-‐mouth on the Social media • Personal experiences and opinions about anything in reviews, forums, blogs, micro-‐ blog, TwiZer. • PosFng at social networking sites, e.g., Facebook • Comments about arFcles, issues, topics, reviews. Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 5
Social media + beyond • Global scale – No longer – one’s circle of friends. • OrganizaFon internal data – Customer feedback from emails, call center • News and reports – Opinions in news arFcles and commentaries Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 6
Social Media and the Voice of the Customer • Listen to the Voice of the Customer (VoC) – Social media can give companies a torrent of highly valuable customer feedback. – Such input is largely free – Customer feedback issued through social media is qualitaFve data, just like the data that market researchers derive from focus group and in-‐depth interviews – Such qualitaFve data is in digital form – in text or digital video on a web site. Source: Robert Wollan, Nick Smith, Catherine Zhou, The Social Media Management Handbook, John Wiley, 2011. 7
Listen and Learn Text Mining for VoC • CategorizaFon – Understanding what topics people are talking or wriFng about in the unstructured porFon of their feedback. • SenFment Analysis – Determining whether people have posiFve, negaFve, or neutral views on those topics. Source: Robert Wollan, Nick Smith, Catherine Zhou, The Social Media Management Handbook, John Wiley, 2011. 8
Opinion Mining and Sen&ment Analysis • Mining opinions which indicate posiFve or negaFve senFments • Analyzes people’s opinions, appraisals, ahtudes, and emoFons toward enFFes, individuals, issues, events, topics, and their aZributes. Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 9
Opinion Mining and Sen&ment Analysis • ComputaFonal study of opinions, senFments, subjecFvity, evaluaFons, ahtudes, appraisal, affects, views, emoFons, ets., expressed in text. – Reviews, blogs, discussions, news, comments, feedback, or any other documents Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 10
Terminology • SenFment Analysis is more widely used in industry • Opinion mining / SenFment Analysis are widely used in academia • Opinion mining / SenFment Analysis can be used interchangeably Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 11
Example of Opinion: review segment on iPhone “I bought an iPhone a few days ago. It was such a nice phone. The touch screen was really cool. The voice quality was clear too. However, my mother was mad with me as I did not tell her before I bought it. She also thought the phone was too expensive, and wanted me to return it to the shop. … ” Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 12
Example of Opinion: review segment on iPhone “(1) I bought an iPhone a few days ago. (2) It was such a nice phone. (3) The touch screen was really cool. +Positive Opinion (4) The voice quality was clear too. (5) However, my mother was mad with me as I did not tell her before I bought it. (6) She also thought the phone was too expensive, and wanted me to return it to the shop. … ” -Negative Opinion Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 13
Why are opinions important? • “Opinions” are key influencers of our behaviors. • Our beliefs and percepFons of reality are condiFoned on how others see the world. • Whenever we need to make a decision, we omen seek out the opinion of others. In the past, – Individuals • Seek opinions from friends and family – OrganizaFons • Use surveys, focus groups, opinion pools, consultants Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 14
Applica&ons of Opinion Mining • Businesses and organizaFons – Benchmark products and services – Market intelligence • Business spend a huge amount of money to find consumer opinions using consultants, surveys, and focus groups, etc. • Individual – Make decision to buy products or to use services – Find public opinions about poliFcal candidates and issues • Ads placements: Place ads in the social media content – Place an ad if one praises a product – Place an ad from a compeFtor if one criFcizes a product • Opinion retrieval: provide general search for opinions. Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 15
Research Area of Opinion Mining • Many names and tasks with difference objecFve and models – SenFment analysis – Opinion mining – SenFment mining – SubjecFvity analysis – Affect analysis – EmoFon detecFon – Opinion spam detecFon Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition, 16
Exis&ng Tools (“Social Media Monitoring/Analysis") • Radian 6 • Social MenFon • Overtone OpenMic • Microsom Dynamics Social Networking Accelerator • SAS Social Media AnalyFcs • Lithium Social Media Monitoring • RightNow Cloud Monitor Source: Wiltrud Kessler (2012), Introduction to Sentiment Analysis 17
Exis&ng Tools (“Social Media Monitoring/Analysis") • Radian 6 • Social MenFon • Overtone OpenMic • Microsom Dynamics Social Networking Accelerator • SAS Social Media AnalyFcs • Lithium Social Media Monitoring • RightNow Cloud Monitor Source: Wiltrud Kessler (2012), Introduction to Sentiment Analysis 18
Word-‐of-‐mouth Voice of the Customer • 1. AZensity – Track social senFment across brands and compeFtors – hZp://www.aZensity.com/home/ • 2. Clarabridge – SenFment and Text AnalyFcs Somware – hZp://www.clarabridge.com/ 19
Attensity: Track social sentiment across brands and competitors http://www.attensity.com/ http://www.youtube.com/watch?v=4goxmBEg2Iw#! 20
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