PPT to show the importance of Negation Identification and handling of Negation in Sentiment Classification of a given sentence.

1. Sentiment Analysis of Social Issues
-Presented By
Shailendra Kumar Singh
Roll No.:- ME/SE/10003/13
M.E- Software Engineering
Computer Science & Eng.,
Department, BIT, Mesra.
-Under Guidance of
Dr. Sanchita Paul
(Assistant Professor)
Computer Science & Eng.,
Department, BIT, Mesra.

2. Outline
• Work Flow Chart
• Introduction
• Negative Sentence
• Negation Words
• Negation Handling Rules
• Need of Negation Handling Rules
• Result Comparison (Negative Comments)
• Positive Sentence
• Result Analysis (Positive & Negative Comments)
• Drawback
• Future Work

3. Work Flow Chart
• Work Flow Chart.docx

4. Introduction
• Our approach is verb oriented sentiment
classification method.
• It works at sentence and opinion level.
• We extract opinion verb and calculate its’
sentiment score from opinion verb dictionary.
• Binary Classification:-
Positive or Negative

5. System Architecture

6. Negative Sentence due to
Negative verb

7. Negation Words
• These words change the sentiment of sentence from
 Positive to Negative and
 Negative to Positive.
List of Negation Words
• nor
• useless
• no
• never
• not
• without
• against

8. “useless” Word is not consider as
Negation word.

9. When “useless” word is consider as
Negation word.

10. “against” Word is not consider as
Negation word.

11. When “against” Word is consider
as Negation word.

12. Negation Handling Rules
Negation
Word
Opinion
verb
Sentiment
score
Positive/
negative
Sentiment score calculation
Yes Positive Negative negative_score =
negative_score – 1 * word_score
Yes Negative Positive positive_score =
positive_score – 1 * word_score
Yes Verb not in
dictionary
Negative negative_score =
negative_score + (-0.125)

13. RULE-1
negative_score = negative_score – 1 * word_score

14. RULE-2
positive_score = positive_score – 1 * word_score

15. RULE-3
negative_score = negative_score + (-0.125)

16. Why we need Negation Handling
Rules?
• I love Mexican food.
Love= sentiment score (0.5)
Sent_score= 0.5
• I do not hate Mexican food.
Hate= sentiment score (-0.75)
Sent_score = -1 * -0.75 =0.75
• Word “not hate” is synonyms of “love”. But sentiment
score of “hate (0.75)” and “love (0.5)” is not equal.

17. Result Comparison
• RESULT COMPARISON.docx

18. Positive Sentence.
• Identification & Sentiment Score Calculation
of Positive Sentence.

19. Kind of Positive Sentence
1- Simple Positive Sentence due to positive verb.
2- Negative Sentence become Positive Sentence
due to Negation Word

20. Simple Positive Sentence due to
positive verb.

21. Negative Sentence become Positive
Sentence due to Negation Word

22. Result Comparison
Positive Comments=39
Negative Comments= 41
Negation Word= -0.125
METHOD DOMAIN ACCURACY
Somasundaran,2010 [2] Social Issues 62.5%
M. Karambekr et al,2012
[1]
Social Issues
(verb, adjective, adverb)
65%
Our Method Social Issues (verb) (79.166 – 85.135) %

23. Drawbacks
1- failed to identify the negation word at last
position in any subjective sentence .

24. 2- Failed to Identify -Verb, past
participle

25. Future Work
• Result Analysis –at different values of
Negation words.
• Classification of Topic into +ve / -ve sentence.
• Collection of Comments- on other topic.
• Thesis Writing.

26. Classification of Topic into +ve / -ve
sentence.
• “Is the Use of Standardized Tests Useless for
Education in America?”
• Comments:-
1- Yes, standardized tests are useless for 5th class
students. (Sentiment is +ve).
2- Standardized Tests can improve Education in
America.(Sentiment is –ve).

27. References
[1] M.Karamibekr & Ali A. Ghorbani. “Sentiment Analysis of Social Issues”. International
Conference on Social Informatics (IEEE) 2012.
[2] S. Somasundaran and J. Wiebe. Recognizing stances in ideological online debates. In Workshop
on Computational Approaches to Analysis and Generation of Emotion in Text, pages 116–124.
ACM, 2010.
[3] M.Dadvar, C. Hauff, and F.de Jong. “Scope of Negation Detection in Sentiment Analysis.” In
11th Dutch-Belgian Information Retrieval Workshop (DIR 2011), 2011, pp. 16-19.
[4] B. Liu. Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2010.
[5] Livia Polanyi and Annie Zaenen. “Contextual Valence Shifters”. In Proceedings of the AAAI
Spring Symposium on Exploring Attitude and Affect in Text, 2012.
[6] http://sentiwordnet.isti.cnr.it
[7] http://www.procon.org/

28. Thank You