Abstract
Online Social networks are widely used in current times. In this paper, we investigate twitter posts to identify features that feed in intention mining calculation. The posts features are divided into three sets: tweets textual features, users features, and network contextual features. In this paper, our focus is on tweets analysing textual features. As a result of this paper, we were able to create intentions profiles for 2960 users based on textual features. The prediction accuracy of three classifiers was compared for the data set, using ten intention categories to test the features. The best accuracy was achieved for SVM classifier. In the future, we plan to include user and network contextual features aiming at improving the prediction accuracy.
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Mishael, Q., Ayesh, A., Yevseyeva, I. (2019). Users Intention Based on Twitter Features Using Text Analytics. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_14
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DOI: https://doi.org/10.1007/978-3-030-33607-3_14
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