Machine Learning and feature engineering-based study into sarcasm and irony classification with application to cyberbullying detection

摘要

Irony and sarcasm detection is considered a complex task in Natural Language Processing. This paper set out to explore the sarcasm and irony on Twitter, using Machine Learning and Feature Engineering techniques. First we review and clarify the definition of irony and sarcasm by discussing various studies focusing on the terms. Next the first experiment is conducted comparing between various types of classification methods including some popular classifiers for text classification task. For the second experiment, different types of data preprocessing methods were compared and analyzed. Finally, the relationship between irony, sarcasm, and cyberbullying are discussed. The results are interesting as we observed high similarity between them.