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

21 Feb 2017Carlos-Emiliano González-GallardoJuan-Manuel Torres-MorenoAzucena Montes RendónGerardo 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.)... (read more)

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper