Lecture 3: Structuring Unstructured Texts Through Sentiment Analysis

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Objective of sentiment analysis: Given an opinion document d, discover all opinion quintuples (ei, aij, sijkl, hk, tl) in d. With these quintuples, unstructured data --> structured data (Bing Liu, …

Objective of sentiment analysis: Given an opinion document d, discover all opinion quintuples (ei, aij, sijkl, hk, tl) in d. With these quintuples, unstructured data --> structured data (Bing Liu, Sentiment Analysis and Opinion Mining. 2012)

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  • 1. Semantic Analysis in Language Technology Lecture 3 - Semantic-Oriented Applications: Sentiment Analysis Course Website: http://stp.lingfil.uu.se/~santinim/sais/sais_fall2013.htm MARINA SANTINI PROGRAM: COMPUTATIONAL LINGUISTICS AND LANGUAGE TECHNOLOGY DEPT OF LINGUISTICS AND PHILOLOGY UPPSALA UNIVERSITY, SWEDEN 21 NOV 2013
  • 2. Acknowledgements 2  Thanks to Bing Liu for the many slides I borrowed from his Tutorial on Sentiment Analysis and Opinion Mining. Big thanks to Dan Jurafsky for his slides from Coursera NLP course. Lecture 3: Sentiment Analysis
  • 3. 3 Lecture 3: Sentiment Analysis
  • 4. Why are sentiments important (opinions/emotions/affects/attitudes/etc) 4 Lecture 3: Sentiment Analysis
  • 5. 5 Lecture 3: Sentiment Analysis
  • 6. 6 Lecture 3: Sentiment Analysis
  • 7. Text Categorization Problem 7  Different level of granularity:  Document  Sentence  Summary Lecture 3: Sentiment Analysis
  • 8. 8 Lecture 3: Sentiment Analysis
  • 9. Opionion: Formalization: Quadruple (4 components) 9 Lecture 3: Sentiment Analysis
  • 10. Whatch out! 10  Date: The date is important in practice because one often wants to know how opinions change with time and opinion trends. Lecture 3: Sentiment Analysis
  • 11. 11 Lecture 3: Sentiment Analysis
  • 12. 12 Lecture 3: Sentiment Analysis
  • 13. Opionion: Formalization: Quintuple (5 components) 13 Lecture 3: Sentiment Analysis
  • 14. 14 Lecture 3: Sentiment Analysis
  • 15. 15 Lecture 3: Sentiment Analysis
  • 16. 16 Lecture 3: Sentiment Analysis
  • 17. In which way ”sentiment” belongs to semantics? 17  Semantics is the study of meaning:   It focuses on the relation between signifiers, like words, phrases, signs, and symbols, and what they stand for. Through a semantics, we want to understand human language. Through SA we want to automatically identify the meaning of certain words, phrases, etc. and how they relate to affective states expressed in texts (long, short, oral, written, etc.) Lecture 3: Sentiment Analysis
  • 18. Subjectivity & Emotion 18 Lecture 3: Sentiment Analysis
  • 19. Subjectivity 19 Lecture 3: Sentiment Analysis
  • 20. Emotion 20 Lecture 3: Sentiment Analysis
  • 21. Sentiment, Subjectivity, Emotion 21 Lecture 3: Sentiment Analysis
  • 22. Affect and Affective words… 22 http://research.microsoft.com/en-us/projects/tweetaffect/ Lecture 3: Sentiment Analysis
  • 23. 23 Lecture 3: Sentiment Analysis
  • 24. 24 Lecture 3: Sentiment Analysis
  • 25. Basically… Text Classification! 25  Topic-based classification  Genre identification  Authorship attribution     (plagiarism, authorship/classification of anonymous texts) Spam filters Automatic email classification (folder assignment) Threat identification Etc. Lecture 3: Sentiment Analysis
  • 26. 26 Lecture 3: Sentiment Analysis
  • 27. Opinion Mining in the real world… 27 Lecture 3: Sentiment Analysis
  • 28. UnSupervised Learning 28 Lecture 3: Sentiment Analysis
  • 29. Supervised Classification 29  See Dan’s video presentation! Lecture 3: Sentiment Analysis
  • 30. 30 Lecture 3: Sentiment Analysis
  • 31. 31 Lecture 3: Sentiment Analysis
  • 32. 32 Lecture 3: Sentiment Analysis
  • 33. 33 Lecture 3: Sentiment Analysis
  • 34. 34 Lecture 3: Sentiment Analysis
  • 35. 35 Lecture 3: Sentiment Analysis
  • 36. 36 Lecture 3: Sentiment Analysis
  • 37. 37 Lecture 3: Sentiment Analysis
  • 38. 38 Lecture 3: Sentiment Analysis
  • 39. Team Work: 20 min; Discussion 15 min 39  You are going to apply for funding . You are interested in Horizion 2020 funding scheme (the new European research and innovation funding framework)  You think it is a good idea to create a Mood Index App. Plan with your team mates this new sentiment-based app. Present to the audience the following aspects: 1) 2) 3) 4) 5) 6) 7) Purpose: what is the main use of this new app? (ex, identification of self-distructive behavior, depressive states, sad/happy mood, freindly attitudes, etc.) Target users: who is going to use this app? (young people, parents, etc) Scenario: describe a typical scenario/context where your app is going to be used with fruitful results Computational aspects: Which sentiment classes is the app going to identify? In which language? Which computational model is going to be based upon? The actors: what kind of experts do you need? (ex a computational linguist, a app developer, a psychiatrist, a company taking care of marketing and commercialization, a social worker, school teacher etc.) Societal Benefits: How can the commercialization of your app contribute to decrease unemployment in your country and/or in EU. Any additional aspect you might find relevant. Lecture 3: Sentiment Analysis
  • 40. How to build your own Twitter Sentiment Analysis Tool 40  http://blog.datumbox.com/how-to-build-your-own-twitter-sentiment-analysis-tool/ Lecture 3: Sentiment Analysis
  • 41. 41 This is the end… Thanks for your attention ! Lecture 3: Sentiment Analysis