Efficient Social Network Multilingual Classification using Character, POS n-grams and Dynamic Normalization

2017-02-21 · Carlos-Emiliano González-Gallardo, Juan-Manuel Torres-Moreno, Azucena Montes Rendón, Gerardo Sierra

In this paper we describe a dynamic normalization process applied to social network multilingual documents (Facebook and Twitter) to improve the performance of the Author profiling task for short texts. After the normalization process, $n$-grams of characters and n-grams of POS tags are obtained to extract all the possible stylistic information encoded in the documents (emoticons, character flooding, capital letters, references to other users, hyperlinks, hashtags, etc.). Experiments with SVM showed up to 90% of performance.

📄 PDF Abstract BibTeX arXiv:1702.06467

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Author ProfilingGeneral ClassificationPOS

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