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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2019
    • vol. 5, nº 7, december 2019
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2019
    • vol. 5, nº 7, december 2019
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    Automatic Irony Detection using Feature Fusion and Ensemble Classifier

    Autor: 
    Harish, B S
    ;
    Kumar, Keerthi
    Fecha: 
    12/2019
    Palabra clave: 
    classification; clustering; feature selection; ensemble methods; sentiment analysis; feature fusion; irony; K-means; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/12659
    DOI: 
    http://doi.org/10.9781/ijimai.2019.07.002
    Dirección web: 
    https://www.ijimai.org/journal/bibcite/reference/2732
    Open Access
    Resumen:
    With the advent of micro-blogging sites, users are pioneer in expressing their sentiments and emotions on global issues through text. Automatic detection and classification of sentiments like sarcastic or ironic content in microblogging reviews is a challenging task. It requires a system that manages some kind of knowledge to interpret the sentiment expressed in text. The available approaches are quite limited in their capabilities and scope to detect ironic utterances present in the text. In this regards, the paper propose feature fusion to provide knowledge to the system by alternative sets of features obtained using linguistic and content based text features. The proposed work extracts five sets of linguistic features and fuses with features selected using two stages of a feature selection method. In order to demonstrate the effectiveness of the proposed method, we conduct extensive experimentation by selecting different feature subsets. The performances of the proposed method are evaluated using Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and ensemble classifiers. The experimental result shows the proposed approach significantly out-performs the conventional methods.
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