Abstract
In this article we propose a supervised method for expanding tweet contents to improve the recall of tweet filtering task in online reputation management systems. Our method does not use any external resources. It consists of creating a K-NN classifier in three steps. In these steps the tweets labeled related and unrelated in the training set are expanded by extracting and adding the most discriminative terms, calculating and adding the most frequent terms, and re-weighting the original tweet terms from training set. Our experiments in RepLab 2013 data set show that our method improves the performance of filtering task, in terms of F criterion, up to 13% over state-of-the-art classifiers such as SVM. This data set consists of 61 entities from different domains of automotive, banking, universities, and music.
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Karisani, P., Oroumchian, F., Rahgozar, M. (2015). Tweet Expansion Method for Filtering Task in Twitter. In: Mothe, J., et al. Experimental IR Meets Multilinguality, Multimodality, and Interaction. CLEF 2015. Lecture Notes in Computer Science(), vol 9283. Springer, Cham. https://doi.org/10.1007/978-3-319-24027-5_5
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DOI: https://doi.org/10.1007/978-3-319-24027-5_5
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