01393nas a2200289   4500000000100000000000100001008004100002260001200043653001900055653001400074653002500088653002400113653001100137653000800148100002200156700002800178700001800206700002000224700002100244700002000265245007300285856009800358300001000456490000600466520061700472022001401089       2014                            d  c03/201410aClassification10aHappiness10aTopic Classification10aText Classification10aGraphs10aNLP1 aHéctor Cordobés1 aAntonio Fernández Anta1 aLuis Chiroque1 aFernando Pérez1 aTeófilo Redondo1 aAgustín Santos00aGraph-based Techniques for Topic Classification of Tweets in Spanish  uhttp://www.ijimai.org/journal/sites/default/files/files/2014/03/ijimai20142_5_4_pdf_17528.pdf  a31-370 v23 aTopic classification of texts is one of the most interesting challenges in Natural Language Processing (NLP). Topic classifiers commonly use a bag-of-words approach, in which the classifier uses (and is trained with) selected terms from the input texts. In this work we present techniques based on graph similarity to classify short texts by topic. In our classifier we build graphs from the input texts, and then use properties of these graphs to classify them. We have tested the resulting algorithm by classifying Twitter messages in Spanish among a predefined set of topics, achieving more than 70% accuracy.  a1989-1660