Seminar presentation made by me for the topic of 'Resources for Sentiment Analysis' at IIT Bombay. Includes a set of bonus slides for additional information which was not actually presented.

1. Resources for Sentiment Analysis
Seminar Presentation
Sagar Ahire
133050073
IIT Bombay
02 May, 2014
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 1 / 48

2. Roadmap
1 Introduction
2 Sentiwordnet
3 SO-CAL
4 Wordnet-Affect
5 Indian-Language Sentiwordnets
6 Conclusions
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3. Introduction
Roadmap: We Are Here
1 Introduction
2 Sentiwordnet
3 SO-CAL
4 Wordnet-Affect
5 Indian-Language Sentiwordnets
6 Conclusions
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4. Introduction Overview
Overview
An overview of today’s presentation:
This presentation covers lexical resources for sentiment analysis.
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 4 / 48

5. Introduction Overview
Overview
An overview of today’s presentation:
This presentation covers lexical resources for sentiment analysis.
Four resources are covered, each using a different approach for
representation and creation:
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 4 / 48

6. Introduction Overview
Overview
An overview of today’s presentation:
This presentation covers lexical resources for sentiment analysis.
Four resources are covered, each using a different approach for
representation and creation:
Sentiwordnet, created automatically, with 3 graded scores per synset
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 4 / 48

7. Introduction Overview
Overview
An overview of today’s presentation:
This presentation covers lexical resources for sentiment analysis.
Four resources are covered, each using a different approach for
representation and creation:
Sentiwordnet, created automatically, with 3 graded scores per synset
SO-CAL, created manually, with a graded score per word
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 4 / 48

8. Introduction Overview
Overview
An overview of today’s presentation:
This presentation covers lexical resources for sentiment analysis.
Four resources are covered, each using a different approach for
representation and creation:
Sentiwordnet, created automatically, with 3 graded scores per synset
SO-CAL, created manually, with a graded score per word
Wordnet-Affect, created semi-automatically, with affect information for
each synset
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 4 / 48

9. Introduction Overview
Overview
An overview of today’s presentation:
This presentation covers lexical resources for sentiment analysis.
Four resources are covered, each using a different approach for
representation and creation:
Sentiwordnet, created automatically, with 3 graded scores per synset
SO-CAL, created manually, with a graded score per word
Wordnet-Affect, created semi-automatically, with affect information for
each synset
Indian-Language Sentiwordnet, created by projecting the English
Sentiwordnet
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 4 / 48

10. Introduction Sentiment Analysis
Sentiment Analysis
Sentiment Analysis: Determining the opinion expressed in a text
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11. Introduction Sentiment Analysis
Sentiment Analysis
Sentiment Analysis: Determining the opinion expressed in a text
Approaches:
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12. Introduction Sentiment Analysis
Sentiment Analysis
Sentiment Analysis: Determining the opinion expressed in a text
Approaches:
Classifier-based
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13. Introduction Sentiment Analysis
Sentiment Analysis
Sentiment Analysis: Determining the opinion expressed in a text
Approaches:
Classifier-based
Lexicon-based
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14. Introduction Sentiment Analysis
Why Lexicon-based Approach?
The classifier-based approach has the following drawbacks:
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15. Introduction Sentiment Analysis
Why Lexicon-based Approach?
The classifier-based approach has the following drawbacks:
Domain Specificity (Example: Movie reviews mentioning ‘writer’,
‘plot’, etc.) [Bro01]
Lack of Context (Example: ‘good’ vs ‘not good’ vs ‘not very good’)
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16. Introduction Sentiment Analysis
Why Lexicon-based Approach?
The classifier-based approach has the following drawbacks:
Domain Specificity (Example: Movie reviews mentioning ‘writer’,
‘plot’, etc.) [Bro01]
Lack of Context (Example: ‘good’ vs ‘not good’ vs ‘not very good’)
The lexicon-based approach aims at solving these problems.
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17. Introduction Sentiment Lexicons
Sentiment Lexicons
A sentiment lexicon is a sentiment database for language units of the form
(lexical unit, sentiment).
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18. Introduction Sentiment Lexicons
Sentiment Lexicons
A sentiment lexicon is a sentiment database for language units of the form
(lexical unit, sentiment).
Choices for lexical unit:
Word
Word sense
Phrase, etc.
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19. Introduction Sentiment Lexicons
Sentiment Lexicons
A sentiment lexicon is a sentiment database for language units of the form
(lexical unit, sentiment).
Choices for lexical unit:
Word
Word sense
Phrase, etc.
Choices for sentiment:
Fixed categorization into ‘positive’ and ‘negative’
Graded sets like ‘strongly positive’, ‘mildly positive’, ‘neutral’, ‘mildly
negative’, ‘strongly negative’
Score in an interval like [0, 1] or [−1, +1]
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20. Introduction Sentiment Lexicons
Approaches for Creation
Manual
Automatic
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21. Sentiwordnet
Roadmap: We Are Here
1 Introduction
2 Sentiwordnet
3 SO-CAL
4 Wordnet-Affect
5 Indian-Language Sentiwordnets
6 Conclusions
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22. Sentiwordnet
Introduction to Sentiwordnet
Sentiwordnet [ES06] is an automatically generated sentiment lexicon made
using Wordnet. Its salient features are:
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23. Sentiwordnet
Introduction to Sentiwordnet
Sentiwordnet [ES06] is an automatically generated sentiment lexicon made
using Wordnet. Its salient features are:
High coverage
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 10 / 48

24. Sentiwordnet
Introduction to Sentiwordnet
Sentiwordnet [ES06] is an automatically generated sentiment lexicon made
using Wordnet. Its salient features are:
High coverage
Support for graded sentiment labels
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 10 / 48

25. Sentiwordnet
Introduction to Sentiwordnet
Sentiwordnet [ES06] is an automatically generated sentiment lexicon made
using Wordnet. Its salient features are:
High coverage
Support for graded sentiment labels
Support for both sentiment classification and subjectivity detection
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26. Sentiwordnet Structure
Structure of Sentiwordnet
Sentiwordnet = Wordnet + Sentiment Information.
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27. Sentiwordnet Structure
Structure of Sentiwordnet
Sentiwordnet = Wordnet + Sentiment Information.
Each synset s is given three sentiment scores:
Positive score Pos(s)
Negative score Neg(s)
Objective score Obj(s)
Pos(s) + Neg(s) + Obj(s) = 1
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28. Sentiwordnet Structure
Structure of Sentiwordnet
Sentiwordnet = Wordnet + Sentiment Information.
Each synset s is given three sentiment scores:
Positive score Pos(s)
Negative score Neg(s)
Objective score Obj(s)
Pos(s) + Neg(s) + Obj(s) = 1
Example Synset
beautifula: Pos = 0.75, Neg = 0.00, Obj = 0.25
a
URL: http://sentiwordnet.isti.cnr.it/search.php?q=beautiful
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29. Sentiwordnet Creation
Creation Steps
The top-level steps in the algorithm to create Sentiwordnet are as follows:
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30. Sentiwordnet Creation
Creation Steps
The top-level steps in the algorithm to create Sentiwordnet are as follows:
1 Selection of seed set
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31. Sentiwordnet Creation
Creation Steps
The top-level steps in the algorithm to create Sentiwordnet are as follows:
1 Selection of seed set
2 Expansion using Wordnet’s semantic relations
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32. Sentiwordnet Creation
Creation Steps
The top-level steps in the algorithm to create Sentiwordnet are as follows:
1 Selection of seed set
2 Expansion using Wordnet’s semantic relations
3 Training of a team of ternary classifiers
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 12 / 48

33. Sentiwordnet Creation
Creation Steps
The top-level steps in the algorithm to create Sentiwordnet are as follows:
1 Selection of seed set
2 Expansion using Wordnet’s semantic relations
3 Training of a team of ternary classifiers
4 Classification of each Wordnet synset using the classifiers
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 12 / 48

34. SO-CAL
Roadmap: We Are Here
1 Introduction
2 Sentiwordnet
3 SO-CAL
4 Wordnet-Affect
5 Indian-Language Sentiwordnets
6 Conclusions
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35. SO-CAL
Introduction to SO-CAL
SO-CAL is a system that uses a manually-constructed lexicon. Its salient
features are:
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36. SO-CAL
Introduction to SO-CAL
SO-CAL is a system that uses a manually-constructed lexicon. Its salient
features are:
Highly detailed lexicon
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37. SO-CAL
Introduction to SO-CAL
SO-CAL is a system that uses a manually-constructed lexicon. Its salient
features are:
Highly detailed lexicon
Graded sentiment label
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38. SO-CAL
Introduction to SO-CAL
SO-CAL is a system that uses a manually-constructed lexicon. Its salient
features are:
Highly detailed lexicon
Graded sentiment label
Low coverage, but high accuracy
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 14 / 48

39. SO-CAL Structure
Features Used
SO-CAL classifies words into various features and treats each feature
differently in the lexicon. They are:
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40. SO-CAL Structure
Features Used
SO-CAL classifies words into various features and treats each feature
differently in the lexicon. They are:
Adjectives
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41. SO-CAL Structure
Features Used
SO-CAL classifies words into various features and treats each feature
differently in the lexicon. They are:
Adjectives
Nouns, Verbs, Adverbs and Multiwords
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 15 / 48

42. SO-CAL Structure
Features Used
SO-CAL classifies words into various features and treats each feature
differently in the lexicon. They are:
Adjectives
Nouns, Verbs, Adverbs and Multiwords
Intensifiers and Downtoners
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 15 / 48

43. SO-CAL Structure
Features Used
SO-CAL classifies words into various features and treats each feature
differently in the lexicon. They are:
Adjectives
Nouns, Verbs, Adverbs and Multiwords
Intensifiers and Downtoners
Negation
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 15 / 48

44. SO-CAL Structure
Features Used
SO-CAL classifies words into various features and treats each feature
differently in the lexicon. They are:
Adjectives
Nouns, Verbs, Adverbs and Multiwords
Intensifiers and Downtoners
Negation
Irrealis Blocking
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 15 / 48

45. SO-CAL Structure
Structure of SO-CAL
Sentiment scoring:
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46. SO-CAL Structure
Structure of SO-CAL
Sentiment scoring:
Words are scored in [−5, +5]
Intensifiers and negation further act upon these scores
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47. SO-CAL Structure
Structure of SO-CAL
Sentiment scoring:
Words are scored in [−5, +5]
Intensifiers and negation further act upon these scores
Examples
good: +3
monstrosity: −5
masterpiece: +5
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48. Wordnet-Affect
Roadmap: We Are Here
1 Introduction
2 Sentiwordnet
3 SO-CAL
4 Wordnet-Affect
5 Indian-Language Sentiwordnets
6 Conclusions
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49. Wordnet-Affect
Introduction to Wordnet-Affect
Wordnet-Affect [SV04] is a semi-automatically generated sentiment lexicon
made using Wordnet. It associates affective information with each
synset. Its salient features are:
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50. Wordnet-Affect
Introduction to Wordnet-Affect
Wordnet-Affect [SV04] is a semi-automatically generated sentiment lexicon
made using Wordnet. It associates affective information with each
synset. Its salient features are:
Highly detailed
Ability to handle sentiment differently depending on emotion
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51. Wordnet-Affect Structure
Structure of Wordnet-Affect
Wordnet-Affect = Wordnet + Affect Information.
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52. Wordnet-Affect Structure
Structure of Wordnet-Affect
Wordnet-Affect = Wordnet + Affect Information.
Affect is represented using the following:
An a-label which represents the emotion,
The valency which indicates the sentiment.
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53. Wordnet-Affect Structure
Structure of Wordnet-Affect
The a-label is a tree of emotions starting at a root node with each
leaf node corresponding to a synset.
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54. Wordnet-Affect Structure
Structure of Wordnet-Affect
The a-label is a tree of emotions starting at a root node with each
leaf node corresponding to a synset.
The valency can be any of positive, negative, neutral or ambiguous.
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 20 / 48

55. Wordnet-Affect Structure
root
mental-state
cognitive-state affective-state
mood emotion
positive-emotion
joy
elation
love
worship
negative-emotion
sadness
melancholy
shame
embarrassment
. . .
. . .
physical-state . . .
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56. Wordnet-Affect Creation
Creation Steps
Wordnet-Affect was created using the following steps:
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57. Wordnet-Affect Creation
Creation Steps
Wordnet-Affect was created using the following steps:
Manual creation of initial resource
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58. Wordnet-Affect Creation
Creation Steps
Wordnet-Affect was created using the following steps:
Manual creation of initial resource
Automatic expansion using Wordnet relations
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59. Indian-Language Sentiwordnets
Roadmap: We Are Here
1 Introduction
2 Sentiwordnet
3 SO-CAL
4 Wordnet-Affect
5 Indian-Language Sentiwordnets
6 Conclusions
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60. Indian-Language Sentiwordnets
Introduction to Indian-Language Sentiwordnets
Indian-language Sentiwordnets can be created using Wordnet projection
[JRB10]. This approach has the following salient features:
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61. Indian-Language Sentiwordnets
Introduction to Indian-Language Sentiwordnets
Indian-language Sentiwordnets can be created using Wordnet projection
[JRB10]. This approach has the following salient features:
Easy to create once backing resources are available
No reduplication of effort
Use of tried-and-tested representations
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62. Indian-Language Sentiwordnets Creation
Creation Steps
The process of projecting a Sentiwordnet has the following steps:
Fetch a synset from the English Sentiwordnet.
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63. Indian-Language Sentiwordnets Creation
Creation Steps
The process of projecting a Sentiwordnet has the following steps:
Fetch a synset from the English Sentiwordnet.
Find the corresponding Hindi synset using Indowordnet.
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64. Indian-Language Sentiwordnets Creation
Creation Steps
The process of projecting a Sentiwordnet has the following steps:
Fetch a synset from the English Sentiwordnet.
Find the corresponding Hindi synset using Indowordnet.
Assign sentiment scores from English synset to Hindi synset.
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65. Conclusions
Roadmap: We Are Here
1 Introduction
2 Sentiwordnet
3 SO-CAL
4 Wordnet-Affect
5 Indian-Language Sentiwordnets
6 Conclusions
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66. Conclusions
A Comparison of the Resources
Criterion SWN SO-CAL WN-Affect IL-SWN
Sentiment 3 x [0, 1] [−5, +5] Affect 3 x [0, 1]
Lexical Unit Synset Word Synset Synset
Backing Resource Wordnet None Wordnet SWN + In-
dowordnet
Creation Automatic Manual Automatic Projection
No of Entries 117,000 5,000 900 16,000
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67. Conclusions
Concluding Remarks
To conclude, there are three choices in making a sentiment lexicon:
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68. Conclusions
Concluding Remarks
To conclude, there are three choices in making a sentiment lexicon:
Creation Approach: Manual, Automatic, Semi-Automatic or
Projection
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69. Conclusions
Concluding Remarks
To conclude, there are three choices in making a sentiment lexicon:
Creation Approach: Manual, Automatic, Semi-Automatic or
Projection
Lexical Unit: Word, Synset or Higher Representations
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70. Conclusions
Concluding Remarks
To conclude, there are three choices in making a sentiment lexicon:
Creation Approach: Manual, Automatic, Semi-Automatic or
Projection
Lexical Unit: Word, Synset or Higher Representations
Sentiment: Labels, Graded Scores or Affect Information
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71. Conclusions
Concluding Remarks: Creation Approach
Manual Approach Automatic Approach
High annotation accuracy Low annotation accuracy
High time investment Low time investment
More details supported Less details supported
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72. Conclusions
Concluding Remarks: Lexical Unit
Word Synset
Unreliable for polysemous words Reliable for polysemous words
No pre-processing required Requires WSD
Projection is comparatively difficult Projection is comparatively easier
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73. Conclusions
Concluding Remarks: Sentiment
Graded scores have been shown to be better than mere labels in general.
Moreover, a graded score resource can always be converted to a
label-based resource.
Affect information can help in specialized circumstances.
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74. Conclusions
Future Work
Possible directions in the future:
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75. Conclusions
Future Work
Possible directions in the future:
Automatic resources for higher-level lexical units like phrases, trees,
etc.
Manual resources for synsets
Manual lexicons for Indian languages
Techniques for building dynamic resources to incorporate ‘netspeak’
and other slang
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76. Conclusions
References I
Julian Brooke, A semantic approach to automatic text sentiment
analysis, M.A. thesis, Stanford University, 2001.
Andrea Esuli and Fabrizio Sebastiani, SentiWordNet: A publicly
available lexical resource for opinion mining, Proceedings of the 5th
Conference on Language Resources and Evaluation (LREC-06), 2006,
pp. 417–422.
Andrea Esuli, Automatic generation of lexical resources for opinion
mining: Models, algorithms and applications, Ph.D. thesis, Universita
di Pisa, 2008.
Christiane Fellbaum, Wordnet: An electronic lexical database, A
Bradford Book, 1998.
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77. Conclusions
References II
Vasileios Hatzivassiloglou and Kathleen R. McKeown, Predicting the
semantic orientation of adjectives, Proceedings of the 35th Annual
Meeting of the Association for Computational Linguistics and Eighth
Conference of the European Chapter of the Association for
Computational Linguistics, Association for Computational Linguistics,
1997, pp. 174–181.
Aditya Joshi, Balamurali A R, and Pushpak Bhattacharyya, A
fall-back strategy for sentiment analysis in hindi: a case study,
Proceedings of ICON 2010: 8th International Conference on Natural
Language Processing, Macmillan Publishers, India, 2010.
Jaap Kamps, Maarten Marx, Robert J. Mokken, and Maarten
de Rijke, Using wordnet to measure semantic orientations of
adjectives, Proceedings of LREC-04, 4th International Conference on
Language Resources and Evaluation, 2004, pp. 1115–1118.
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 34 / 48

78. Conclusions
References III
Ellen Riloff and Janyce Wiebe, Learning extraction patterns for
subjective expressions, Proceedings of the 2003 Conference on
Empirical Methods in Natural Language Processing, Association for
Computational Linguistics, 2003, pp. 105–112.
Carlo Strapparava and Alessandro Valitutti, WordNet-Affect: an
affective extension of WordNet, Proceedings of the 4th International
Conference on Language Resources and Evaluation (LREC-04), 2004,
pp. 1083–1086.
Peter D. Turney and Michael L. Littman, Measuring praise and
criticism: Inference of semantic orientation from association, ACM
Transactions on Information Systems 21 (2003), no. 4, 315–346.
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79. Additional Slides Wordnet
Wordnet
Wordnet [Fel98] is a lexical database organized by word sense. The
fundamental unit of storage is called a synset.
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80. Additional Slides Wordnet
Wordnet
Wordnet [Fel98] is a lexical database organized by word sense. The
fundamental unit of storage is called a synset.
An Example Synset
brilliant, superba: of surpassing excellence
“a brilliant performance”; “a superb actor”
a
URL: http://wordnetweb.princeton.edu/perl/webwn?s=brilliant
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81. Additional Slides Wordnet
Semantic Relations in Wordnet
Wordnet synsets are linked to each other by relations called semantic
relations. Some of them are:
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82. Additional Slides Wordnet
Semantic Relations in Wordnet
Wordnet synsets are linked to each other by relations called semantic
relations. Some of them are:
Antonymy
Meronymy
Hypernymy
Hyponymy
Similar to, etc.
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83. Additional Slides Wordnet
Semantic Relations in Wordnet
Wordnet synsets are linked to each other by relations called semantic
relations. Some of them are:
Antonymy
Meronymy
Hypernymy
Hyponymy
Similar to, etc.
These relations are helpful in creating the training set for classifying
synsets to create Sentiwordnet.
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84. Additional Slides Background
Sentiment Classification
Initial work that automatically detected the sentiment of a word led to
today’s modern lexicons. This included:
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 38 / 48

85. Additional Slides Background
Sentiment Classification
Initial work that automatically detected the sentiment of a word led to
today’s modern lexicons. This included:
Use of conjunction-separated adjectives [HM97]
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86. Additional Slides Background
Sentiment Classification
Initial work that automatically detected the sentiment of a word led to
today’s modern lexicons. This included:
Use of conjunction-separated adjectives [HM97]
PMI-based Extraction using Web Queries [TL03]
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87. Additional Slides Background
Sentiment Classification
Initial work that automatically detected the sentiment of a word led to
today’s modern lexicons. This included:
Use of conjunction-separated adjectives [HM97]
PMI-based Extraction using Web Queries [TL03]
Graph Expansion using Wordnet [KMMdR04]
Sagar Ahire (IIT Bombay) Sentiment Resources 02 May, 2014 38 / 48

88. Additional Slides Background
Sentiment Classification
Initial work that automatically detected the sentiment of a word led to
today’s modern lexicons. This included:
Use of conjunction-separated adjectives [HM97]
PMI-based Extraction using Web Queries [TL03]
Graph Expansion using Wordnet [KMMdR04]
Classification using Wordnet Glosses [Esu08]
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89. Additional Slides Background
Subjectivity Detection
Work that identifies whether a term is indeed subjective is necessary to
filter out objective words from sentiment classification. This includes:
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90. Additional Slides Background
Subjectivity Detection
Work that identifies whether a term is indeed subjective is necessary to
filter out objective words from sentiment classification. This includes:
Adapting Wordnet Glosses to Subjectivity Detection [Esu08]
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91. Additional Slides Background
Subjectivity Detection
Work that identifies whether a term is indeed subjective is necessary to
filter out objective words from sentiment classification. This includes:
Adapting Wordnet Glosses to Subjectivity Detection [Esu08]
Bootstrapping Subjective Expressions from a Corpus [RW03]
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92. Additional Slides Structure of SO-CAL
Adjectives
Adjectives were collected from a 500-document corpus and annotated with
a sentiment score from −5 to +5.
Examples
good: +3
sleazy: −3
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93. Additional Slides Structure of SO-CAL
Nouns, Verbs, Adverbs, Multiwords
This was extended to other parts of speech and multiword expressions, for
a total of about 5,000 words.
Examples
monstrosity: −5
masterpiece: +5
inspire: +2
funny: +2 vs. act funny: −1
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94. Additional Slides Structure of SO-CAL
Intensifiers and Downtoners
Intensifiers are words that increase sentiment intensity while downtoners
are words that reduce sentiment intensity. For example extraordinarily and
somewhat.
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95. Additional Slides Structure of SO-CAL
Intensifiers and Downtoners
Intensifiers are words that increase sentiment intensity while downtoners
are words that reduce sentiment intensity. For example extraordinarily and
somewhat.
Intensifiers and downtoners are modeled as percentage modifiers.
Examples
slightly: −50%
extraordinarily: +50%
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96. Additional Slides Structure of SO-CAL
Negation
Negation is modeled as a numeric shift of value 4 towards the opposite
sentiment.
Examples
good: +3 ⇒ not good: −1
atrocious: −5 ⇒ not atrocious: −1
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97. Additional Slides Structure of SO-CAL
Irrealis Blocking
An irrealis marker is a word that indicates that the sentiment may not be
reliable because the event hasn’t actually happened. For example, ‘would’,
‘expect’, ‘if’, quotation marks, etc.
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98. Additional Slides Structure of SO-CAL
Irrealis Blocking
An irrealis marker is a word that indicates that the sentiment may not be
reliable because the event hasn’t actually happened. For example, ‘would’,
‘expect’, ‘if’, quotation marks, etc.
Sentences with irrealis markers are ignored for sentiment analysis.
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99. Additional Slides Sentiwordnet Creation
Seed Set
Two seed sets are created:
Lp for positive synsets
Ln for negative synsets
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100. Additional Slides Sentiwordnet Creation
Seed Set
Two seed sets are created:
Lp for positive synsets
Ln for negative synsets
Each synset representation consists of:
The terms
The defninition
The sample phrases
Explicit indication of negation
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101. Additional Slides Sentiwordnet Creation
Wordnet Expansion
Relations of Wordnet used for expansion:
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102. Additional Slides Sentiwordnet Creation
Wordnet Expansion
Relations of Wordnet used for expansion:
Direct antonymy
Similarity
Derived from
Pertains to
Attribute
Also see
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103. Additional Slides Sentiwordnet Creation
Classifiers
8 classifiers were created differing in:
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104. Additional Slides Sentiwordnet Creation
Classifiers
8 classifiers were created differing in:
No of iterations of expansion (0, 2, 4, 6)
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105. Additional Slides Sentiwordnet Creation
Classifiers
8 classifiers were created differing in:
No of iterations of expansion (0, 2, 4, 6)
Learning algorithm (SVM, Rocchio)
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106. Additional Slides Sentiwordnet Creation
Classifiers
Each ternary classifier is a sum of 2 binary classifiers:
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107. Additional Slides Sentiwordnet Creation
Classifiers
Each ternary classifier is a sum of 2 binary classifiers:
Positive vs. Not Positive
Negative vs. Not Negative
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108. Additional Slides Sentiwordnet Creation
Classifiers
Each ternary classifier is a sum of 2 binary classifiers:
Positive vs. Not Positive
Negative vs. Not Negative
The results are combined as:
Positive Not Positive
Negative Objective Negative
Not Negative Positive Objective
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