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1. Progress Seminar
M-Tech Computer Science And Engineering
“Detection of Sarcastic Emotion using Genetic Optimization”
By
Vaishnavi Vhora
M.Tech
Computer Science And Engineering
Guru Nanak Institute of Technology, Nagpur
Guide
Prof. Vijaya Kamble
Computer Science And Engineering
Guru Nanak Institute of Technology,
Nagpur

2. CONTENT
• Introduction
• Objective
• Implementation
• Methodology
• Application Snapshot
• References
• Evaluation
• Future work

3. Introduction
• Sarcasm refers to the use of words that mean the opposite of
what you really want to say.
• Oxford dictionary express sarcasm as "the use of sarcasm to
Express or convey contempt".
• Example: "All your products are incredibly amazing !!!.
• The sarcastic reorganization system is very helpful for the
improvement of automatic sentiment analysis.

4. • Sarcasm Detection: the task of this field is to detect if a given text is sarcastic or not.
• Twitter: Twitter is an American microblogging and social networking service.
• Sentiment analysis: is a natural language processing technique used to determine whether data is
positive, negative or neutral.

5. Objectives
• The main focus is on automated sentiment analysis of existing systems for
improvement and improved.
• We have proposed an algorithm to understand a sentence or tweet is sarcastic or
not.
• To simplifies the difficulty in distinguishing sarcastic sentences from
negative/positive sentences.

6. Implementation
• Hardware Specification:
System: Core i3 and above
Hard Disk:40 GB
Ram: 2GB
• Software Specification:
Language: Python 3.7
Development Tool: PyCharm 2019.1.3
Operating System: Windows 7& above

7. Methodology
Proposed System Process Diagram Flow of handling tweets

8. Methodology
We proposes four sets of features that include a lot of specific sarcasm.
• Dataset
We use Sarcastic tweets, containing #sarcasm, and #not.
• Pre-processing
The document is split into word
Vocabulary building
Encoding
When the algorithm is used on data, like we provide input tweets, and there output is
either positive, negative or neutral.
• Feature Extraction
The features related to sentiment:
The features related to Punctuation:
The features related to syntactic and Semantic:
The feature related to pattern

9. Methodology
• Training and Testing Data
We will perform K-fold cross validation on the data
• Classification
For classification we have use following algorithms:
• Support Vector Machine (SVM)
• Random Forest
• k-nearest neighbors algorithm (k-NN)

10. ApplicationSnapshot
Fig: Browse

11. ApplicationSnapshot
Fig : Read_Dataset

12. ApplicationSnapshot
Remove_Duplicate_Tweet

13. ApplicationSnapshot
Fig: Remove_Short_Text

14. ApplicationSnapshot
Fig: Tweet_Contraction

15. References
Yi Tay, Mondher Bouazizi And Tomoaki Otsuki (Ohtsuki), “A Pattern-Based Approach For Sarcasm
Detection On Twitter” in August 24, 2016
Yi Tay†, Luu Anh Tuan, Siu Cheung Huiφ, JianSuδ, “Reasoning with Sarcasm by Reading In-
between”arXiv:1805.02856v1 [cs.CL] 8 May 2018
Aditya Joshi, Pushpak Bhattacharyya, Mark J. Carman.2018, “Sarcasm Detection Using Incongruity
within Target Text”, In: Investigations in Computational Sarcasm”,Cognitive Systems Monographs,
vol 37. Springer
Aditya Joshi, Pushpak Bhatta charyya,Mark J. Carman.2018, “Sarcasm Detection Using Contextual
Incongruity. . . In: Investigations in Computational Sarcasm”, Cognitive Systems Monographs, vol
37. Springer
Shubhadeep Mukherjee, Dr. Pradip Kumar Bala, “Sarcasm Detection in MicroblogsUsing Naïve
Bayes and Fuzzy Clustering” In Proceedings of Technology in Society,2017 pages 19-27

16. Evaluation
• In this work, we proposed a new method to detect sarcasm on Twitter.
The proposed method makes use of the different components of the
tweet.
• Our approach makes use of Part-of-Speech tags to extract patterns
characterizing the level of sarcasm of tweets.
• We also proposed an efficient way to enrich our set with more sarcastic
patterns using an initial training set of 6000 Tweets, and the hashtag
‘‘#sarcasm’’.

17. Future Work
The approach has shown good results, though might have even better
results if we use a bigger training set since the patterns we extracted
from the current one might not cover all possible sarcastic patterns.

18. THANK YOU