• Project Title: AIrony: Detecting Irony Using Machine Learning

  • BASIS Advisor: Mr. Linhares

  • Internship Location: Haker Dojo

  • Onsite Mentor: Dr. Jelena Mitrovic

My project aims to try and help intelligent systems more efficiently communicate with humans. Detecting figures of speech is important in recommender systems which are used in popular websites such as Amazon and Netflix for personalizing content. Specifically, my project attempts to solve the problem of detecting irony using AI. The goal of this project, conducted at Hacker Dojo, is to experiment with various AI techniques, ranging from deep learning to machine learning to distributional semantics, in order to create a system that accurately detects the two main kinds of verbal irony: context based and context-less. This project will employ recurrent neural networks and other kinds of classifiers, particularly decision tree and support vector machine classifiers. I will first create a multi-step machine learning solution for detecting irony which would involve multiple classifiers focusing on different parts of the task. The ideal goal would be to create a one-size-fits-all model, potentially using deep learning. It would recognize patterns in examples of irony, such as similes, and then determine the likelihood of finding irony given the existence of these patterns. Another main segment of this project involves training word-to-vector convertors (often called word embeddings) for the specific task of detecting irony, essentially “priming” the data for various numerical classifiers. The findings of this project can be used in AI bots specialized in Natural Language Processing.