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This paper aims to fill the gap of the lack of resources for Twitter sentiment analysis in Spanish by performing a study of different features and machine learning algorithms for classifying the polarity of Twitter posts. The result is a new corpus of Spanish tweets called COST, and we have carried out a wide-ranging experiment in which different machine learning algorithms have been used. Furthermore, we have tested the influence of using different weighting schemes for unigrams, the influence of eliminating stop-words and the application of a stemmer process. <\/jats:p>","DOI":"10.1177\/0165551514566564","type":"journal-article","created":{"date-parts":[[2015,2,4]],"date-time":"2015-02-04T03:24:17Z","timestamp":1423020257000},"page":"263-272","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":23,"title":["Polarity classification for Spanish tweets using the COST corpus"],"prefix":"10.1177","volume":"41","author":[{"given":"Eugenio","family":"Mart\u00ednez-C\u00e1mara","sequence":"first","affiliation":[{"name":"Computer Science Department, University of Ja\u00e9n, Spain"}]},{"given":"M. Teresa","family":"Mart\u00edn-Valdivia","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Ja\u00e9n, Spain"}]},{"given":"L. 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