Senior Capstone
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Sarcasm Detection Using Neural Nets

Abstract

Over the last decade, researchers have come to realize that sarcasm detection is more than just another natural language task such as sentiment analysis. Problems like human error and longer processing times pertaining to sarcasm arise because previous researchers manually created features that would detect sarcasm. In an effort to limit these problems, researchers desisted from using the pre-crafted-feature-prediction models and turned to using neural networks to predict sarcasm. To understand sarcasm, one needs to have a bit of background information on the topic, common shared knowledge and also exist in the space in which the sarcastic statement exists. With this in mind, introducing visual aspects of a conversation would help improve the accuracy of a sarcasm prediction model.

Paper
Software Demo Video
Software Architecture Diagram

Idea Number 3
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  1. Name of Your Project

Ans: SARS

  • What research topic/question your project is going to address?

Ans: Using trained neural nets to be able to tell when a statement/sentence is sarcasm 

  • What technology will be used in your project?

Ans: NLTK and 

  • What software and hardware will be needed for your project?

Ans: Botmock is the only software that will be needed for this project

  • How are you planning to implement?

Ans: I plan on making this an extension of Botmock

  • How is your project different from others? What’s new in your project? 

Ans: With my project, I am using the same method of using CNN model hierarchy when it comes sentiment analysis to learn the context and space in which the sentence exists

  • What’s the difficulties of your project? What problems you might encounter during your project?

Ans: Every sarcasm exist in a defined space one difficulty of this project is trying to build a barrier for that space. Another problem would be getting access to Botmock’s API to make this application compatible.