Abstract
The aim of this paper is to evaluate the use of content and style features in automatic classification of intentions of Tweets. For this we propose different style features and evaluate them using a machine learning approach. We found that although the style features by themselves are useful for the identification of the intentions of tweets, it is better to combine such features with the content ones. We present a set of experiments, where we achieved a 9.46 % of improvement on the overall performance of the classification with the combination of content and style features as compared with the content features.
This work was done under partial support of the Mexican Government (CONACYT-134186, CONACYT grant #308719, SNI, COFAA-IPN, SIP-IPN 20144274) and FP7-PEOPLE-2010-IRSES: “Web Information Quality Evaluation Initiative (WIQ-EI)” European Commission project 269180.
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Gómez-Adorno, H., Pinto, D., Montes, M., Sidorov, G., Alfaro, R. (2014). Content and Style Features for Automatic Detection of Users’ Intentions in Tweets. In: Bazzan, A., Pichara, K. (eds) Advances in Artificial Intelligence -- IBERAMIA 2014. IBERAMIA 2014. Lecture Notes in Computer Science(), vol 8864. Springer, Cham. https://doi.org/10.1007/978-3-319-12027-0_10
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