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Predicting election results using Twitter data is an example of how data can directly influence the politic domain and it also serves an appealing research topic. This article aims to predict the results of the 2019 Spanish Presidential election and the voting share of each candidate, using Tweeter. The method combines sentiment analysis and volume information and compares the performance of five Machine learning algorithms. Several data scrutiny uncertainties arose that hindered the prediction of the outcome. Consequently, the method develops a political lexicon-based framework to measure the sentiments of online users. Indeed, an accurate understanding of the contextual content of the tweets posted was vital in this work. Our results correctly ranked the candidates and determined the winner by means of a better prediction of votes than official research institutes.<\/jats:p>","DOI":"10.1186\/s40537-020-00334-5","type":"journal-article","created":{"date-parts":[[2020,8,6]],"date-time":"2020-08-06T08:04:41Z","timestamp":1596701081000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Inferring the votes in a new political landscape: the case of the 2019 Spanish Presidential elections"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1027-1176","authenticated-orcid":false,"given":"Didier","family":"Grimaldi","sequence":"first","affiliation":[]},{"given":"Javier Diaz","family":"Cely","sequence":"additional","affiliation":[]},{"given":"Hugo","family":"Arboleda","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,8,6]]},"reference":[{"key":"334_CR1","doi-asserted-by":"publisher","first-page":"887","DOI":"10.1007\/978-3-030-15035-8_87","volume":"927","author":"B Abu-Salih","year":"2019","unstructured":"Abu-Salih B, Bremie B, Wongthongtham P, Duan K, Issa T, Chan KY. 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