Abstract

This paper describes the ALANIS system that participated in Task 3 of SemEval-2018. We develop a system for detection of irony, as well as the detection of three types of irony: verbal polar irony, other verbal irony, and situational irony. The system uses a logistic regression model in subtask A and a voted classifier system with manually developed features to identify ironic tweets. This model improves on a naive bayes baseline by about 8 percent on training set.

Highlights

  • With the invention and growth of various social networking sites, irony and other creative linguistic devices have become increasingly prevalent in online content

  • Our system finds hashtags that contain words related to global issues, sports, entertainment, and fashion using a manually created list of top hashtags

  • The classifiers we used for our system included Naive Bayes, logistic regression, Support Vector Machine (SVM) and Random Forest

Read more

Summary

Introduction

With the invention and growth of various social networking sites, irony and other creative linguistic devices have become increasingly prevalent in online content. When considering microblogging platforms like Twitter, which encourage users to share their thoughts and opinions on a wide variety of topics, the use of irony can be extremely common. This can have strong implications for various problems in natural language processing, which often have difficulty in processing this ironic content (e.g., (Liu, 2012; Ghosh and Veale, 2016; Maynard and Greenwood, 2014)), motivating the development of an accurate irony detection system. The F1-scores are .469 and .276 on the test set

Task Description
ALANIS
Feature Selection
Structural Features
Affective Features
Classifiers
Experimental Results
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call