PPT-Subjectivity and Sentiment Analysis: from Words to Discour

Jan Wiebe Computer Science Department Intelligent Systems Program University of Pittsburgh From Text to Political Positions 2010 Burgeoning Field Quite a large problem space Several terms reflecting varying goals and models. Jan Wiebe. . Department of Computer Science. Intelligent Systems Program . University of Pittsburgh. Burgeoning Field. Quite a large problem space. Several terms reflecting varying goals and models. Smaranda. . Muresan. Columbia University. smara@ccls.columbia.edu. Sentiment Lexicons. Announcements. Class setup on . Courseworks. too. Class website linked to . Courseworks. (“Class website” tab). Set 6. Sentiment and Opinions. It's about finding out what people think.... Can be big business…. Someone who wants to buy a camera. Looks for reviews online. Someone who just bought a camera. Writes reviews online. Abhijit. Mishra. 1. , . Aditya. Joshi. 1,2,3. , . Pushpak. Bhattacharyya. 1. 1 . IIT Bombay, India . 2 . Monash. University, Australia. 3. IITB-Monash Research Academy. At 5th Workshop on Computational Approaches to Subjectivity, Sentiment & Social Media Analysis, ACL 2014, Baltimore . 04 10, 2014. Hyun . Geun. . Soo. Bo Pang and Lillian Lee (2004) ACL-04. Outline. Introduction. Method. Evaluation Framework. Experimental Results. Conclusions. Intro. Sentiment analysis. Identify the view point underlying a text span. A . Lexical. . Resource. . for. German Sentiment Analysis. University . of. Bielefeld . Ulli . Waltinger. ulli_marc.waltinger@uni-bielefeld.de. LREC2010 . The International Conference on Language Resources and Evaluation. Heng. . Ji. jih@rpi.edu. October . 25, 2016. Acknowledgement: Some slides from Jan . Wiebe. and . Kavita. . Ganesan. . Emotion Examples. A Happy Song? . A Sad Song. ?. http. ://y.qq.com/webplayer/p.html?songList=%5B%5D&type=1&vip=-1&userName=&ipad=0&from=0&singerid=0&encodedUIN=&. Giacomo Righetti, dept. of Computer Science, University of Pisa, ISTI-CNR. Agenda. Part I. Introduction. Motivations . and Issues . User level informations. Topics. Part II. Discussion about topics. Giacomo Righetti, dept. of Computer Science, University of Pisa, ISTI-CNR. 1. What is Subjectivity?. Private state:. state that is not open to objective. observation . or verification. . Quirk, . Greenbaum. , Leech,. . Svartvik. (1985. ) . Note that this particular use of . Jan Wiebe. . Department of Computer Science. Intelligent Systems Program . University of Pittsburgh. MAVIR November 2010. Foci. Natural Language Processing. Understanding text and conversation by computer. Positive or negative movie review?. unbelievably . disappointing . Full of . zany characters and richly applied satire, and some great plot . twists. this is the greatest screwball comedy ever . filmed. Slides from Bing Liu and Ronan Feldman. Introduction. Two main types of textual information. . Facts and Opinions. Note: factual statements can imply opinions too.. Most current text information processing methods (e.g., web search, text mining) work with factual information.. Positive or negative movie review?. unbelievably . disappointing . Full of . zany characters and richly applied satire, and some great plot . twists. this is the greatest screwball comedy ever . filmed. Bettina Berendt. Department . o. f Computer Science. KU Leuven, Belgium. http://people.cs.kuleuven.be/~bettina.berendt. /. Summer School . Foundations and Applications of Social Network Analysis & Mining.