Irony Detection: Lexical and Structural Features

Student: Makeeva Alena

Educational Programme: Language Theory and Computational Linguistics (Master)

Year of Graduation: 2017

The goal of this work is to analyze possible features suitable for irony extraction in Russian texts from microblogging platform Twitter. Previous experiments and approaches to the problem are reviewed. The terms "irony", "sarcasm" and "humor" are defined. A corpus from 9530 "tweets" is collected, analyzed, marked for features and lemmatized. An experiment based on Random Forest Classifier algorithm is made. Algorithms for future research and experiments concerning irony extraction are proposed. Keywords: irony extraction, machine learning, sentiment extraction, Random Forest.

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