Semantic Analysis in Language Technology
Lecture 3 - Semantic-Oriented Applications:
Sentiment Analysis
Course Website: ht...
Acknowledgements
2

 Thanks to Bing Liu for the many slides I borrowed

from his Tutorial on Sentiment Analysis and Opini...
3

Lecture 3: Sentiment Analysis
Why are sentiments important
(opinions/emotions/affects/attitudes/etc)
4

Lecture 3: Sentiment Analysis
5

Lecture 3: Sentiment Analysis
6

Lecture 3: Sentiment Analysis
Text Categorization Problem
7

 Different level of granularity:
 Document
 Sentence
 Summary

Lecture 3: Sentiment Ana...
8

Lecture 3: Sentiment Analysis
Opionion: Formalization: Quadruple (4 components)
9

Lecture 3: Sentiment Analysis
Whatch out!
10

 Date: The date is important in practice because one

often wants to know how opinions change with time
a...
11

Lecture 3: Sentiment Analysis
12

Lecture 3: Sentiment Analysis
Opionion: Formalization: Quintuple (5 components)
13

Lecture 3: Sentiment Analysis
14

Lecture 3: Sentiment Analysis
15

Lecture 3: Sentiment Analysis
16

Lecture 3: Sentiment Analysis
In which way ”sentiment” belongs to semantics?
17


Semantics is the study of
meaning:




It focuses on the relation
b...
Subjectivity & Emotion
18

Lecture 3: Sentiment Analysis
Subjectivity
19

Lecture 3: Sentiment Analysis
Emotion
20

Lecture 3: Sentiment Analysis
Sentiment, Subjectivity, Emotion
21

Lecture 3: Sentiment Analysis
Affect and Affective words…
22
http://research.microsoft.com/en-us/projects/tweetaffect/

Lecture 3: Sentiment Analysis
23

Lecture 3: Sentiment Analysis
24

Lecture 3: Sentiment Analysis
Basically… Text Classification!
25

 Topic-based classification
 Genre identification
 Authorship attribution





...
26

Lecture 3: Sentiment Analysis
Opinion Mining in the real world…
27

Lecture 3: Sentiment Analysis
UnSupervised Learning
28

Lecture 3: Sentiment Analysis
Supervised Classification
29

 See Dan’s video presentation!

Lecture 3: Sentiment Analysis
30

Lecture 3: Sentiment Analysis
31

Lecture 3: Sentiment Analysis
32

Lecture 3: Sentiment Analysis
33

Lecture 3: Sentiment Analysis
34

Lecture 3: Sentiment Analysis
35

Lecture 3: Sentiment Analysis
36

Lecture 3: Sentiment Analysis
37

Lecture 3: Sentiment Analysis
38

Lecture 3: Sentiment Analysis
Team Work: 20 min; Discussion 15 min
39


You are going to apply for funding . You are interested in Horizion 2020 fundin...
How to build your own Twitter Sentiment Analysis Tool
40


http://blog.datumbox.com/how-to-build-your-own-twitter-sentime...
41

This is the end… Thanks for your attention !

Lecture 3: Sentiment Analysis
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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, Sentiment Analysis and Opinion Mining. 2012)

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Lecture 3: Structuring Unstructured Texts Through Sentiment Analysis

  1. 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. 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. 3 Lecture 3: Sentiment Analysis
  4. 4. Why are sentiments important (opinions/emotions/affects/attitudes/etc) 4 Lecture 3: Sentiment Analysis
  5. 5. 5 Lecture 3: Sentiment Analysis
  6. 6. 6 Lecture 3: Sentiment Analysis
  7. 7. Text Categorization Problem 7  Different level of granularity:  Document  Sentence  Summary Lecture 3: Sentiment Analysis
  8. 8. 8 Lecture 3: Sentiment Analysis
  9. 9. Opionion: Formalization: Quadruple (4 components) 9 Lecture 3: Sentiment Analysis
  10. 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. 11 Lecture 3: Sentiment Analysis
  12. 12. 12 Lecture 3: Sentiment Analysis
  13. 13. Opionion: Formalization: Quintuple (5 components) 13 Lecture 3: Sentiment Analysis
  14. 14. 14 Lecture 3: Sentiment Analysis
  15. 15. 15 Lecture 3: Sentiment Analysis
  16. 16. 16 Lecture 3: Sentiment Analysis
  17. 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. 18. Subjectivity & Emotion 18 Lecture 3: Sentiment Analysis
  19. 19. Subjectivity 19 Lecture 3: Sentiment Analysis
  20. 20. Emotion 20 Lecture 3: Sentiment Analysis
  21. 21. Sentiment, Subjectivity, Emotion 21 Lecture 3: Sentiment Analysis
  22. 22. Affect and Affective words… 22 http://research.microsoft.com/en-us/projects/tweetaffect/ Lecture 3: Sentiment Analysis
  23. 23. 23 Lecture 3: Sentiment Analysis
  24. 24. 24 Lecture 3: Sentiment Analysis
  25. 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. 26 Lecture 3: Sentiment Analysis
  27. 27. Opinion Mining in the real world… 27 Lecture 3: Sentiment Analysis
  28. 28. UnSupervised Learning 28 Lecture 3: Sentiment Analysis
  29. 29. Supervised Classification 29  See Dan’s video presentation! Lecture 3: Sentiment Analysis
  30. 30. 30 Lecture 3: Sentiment Analysis
  31. 31. 31 Lecture 3: Sentiment Analysis
  32. 32. 32 Lecture 3: Sentiment Analysis
  33. 33. 33 Lecture 3: Sentiment Analysis
  34. 34. 34 Lecture 3: Sentiment Analysis
  35. 35. 35 Lecture 3: Sentiment Analysis
  36. 36. 36 Lecture 3: Sentiment Analysis
  37. 37. 37 Lecture 3: Sentiment Analysis
  38. 38. 38 Lecture 3: Sentiment Analysis
  39. 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. 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. 41 This is the end… Thanks for your attention ! Lecture 3: Sentiment Analysis
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