Exploring the fine-grained analysis and automatic detection of irony on Twitter

Publication type
Publication status
In press
Van Hee, C., Lefever, E., & Hoste, V.
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To push the state of the art in text mining applications, research in natural language
processing has increasingly been investigating automatic irony detection, but manually
annotated irony corpora are scarce. We present the construction of a manually
annotated irony corpus based on a fine-grained annotation scheme for irony that
allows to identify different irony types. We conduct a series of binary classification
experiments for automatic irony recognition using a support vector machine exploiting
a varied feature set and a deep learning approach making use of an LSTM network
and (pre-trained) word embeddings. Evaluation on a held-out corpus shows that the
SVM model outperforms the neural network approach and benefits from combining
lexical, semantic and syntactic information sources. A qualitative analysis of the
classification output reveals that the classifier performance may be further enhanced
by integrating implicit sentiment information and context- and user-based features.