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(.) The European Research Council (ERC) through grant 320360 and through the European Union's Horizon 2020 research and innovation programme through grantagreement 670519 (Mixing and Angular Momentum tranSport of massIvEstars – MAMSIE); the Spanish Ministry of Economy MINECO-FEDER through grants AyA2014-55216, AyA2011-24052, ESP2013-48318-C2-R, and ESP2014-55996-C2-R and MDM-2014-0369 of ICCUB (Unidad de Excelencia María de Maeztu) (.) ; van Leeuwen, F.; Vallenari, A.; Jordi, C.; Lindegren, L.; Bastian, U.; Prusti, T.; de Bruijne, J. H. J.; Brown, A. G. A.; Babusiaux, C.; Bailer-Jones, C. A. L.; Biermann, M.; Evans, D. W.; Eyer, L.; Jansen, F.; Klioner, S. A.; Lammers, U.; Luri, X.; Mignard, F.; Panem, C.; Pourbaix, D.; Randich, S.; Sartoretti, P.; Siddiqui, H. I.; Soubiran, C.; Valette, V.; Walton, N. A.; Aerts, C.; Arenou, F.; Cropper, M.; Drimmel, R.; Hog, E.; Katz, D.; Lattanzi, M. G.; O'Mullane, W.; Grebel, E. K.; Holland, A. D.; Huc, C.; Passot, X.; Perryman, M.; Bramante, L.; Cacciari, C.; Castaneda, J.; Chaoul, L.; Cheek, N.; De Angeli, F.; Fabricius, C.; Guerra, R.; Hernandez, J.; Jean-Antoine-Piccolo, A.; Masana, E.; Messineo, R.; Mowlavi, N.; Nienartowicz, K.; Ordonez-Blanco, D.; Panuzzo, P.; Portell, J.; Richards, P. J.; Riello, M.; Seabroke, G. M.; Tanga, P.; Thevenin, F.; Torra, J.; Els, S. G.; Gracia-Abril, G.; Comoretto, G.; Garcia-Reinaldos, M.; Lock, T.; Mercier, E.; Altmann, M.; Andrae, R.; Astraatmadja, T. L.; Bellas-Velidis, I.; Benson, K.; Berthier, J.; Blomme, R.; Busso, G.; Carry, B.; Cellino, A.; Clementini, G.; Cowell, S.; Creevey, O.; Cuypers, J.; Davidson, M.; De Ridder, J.; de Torres, A.; Delchambre, L.; Dell'Oro, A.; Ducourant, C.; Fremat, Y.; Garcia-Torres, M.; Gosset, E.; Halbwachs, J. -L.; Hambly, N. C.; Harrison, D. L.; Hauser, M.; Hestroffer, D.; Hodgkin, S. T.; Huckle, H. E.; Hutton, A.; Jasniewicz, G.; Jordan, S.; Kontizas, M.; Korn, A. J.; Lanzafame, A. C.; Manteiga, M.; Moitinho, A.; Muinonen, K.; Osinde, J.; Pancino, E.; Pauwels, T.; Petit, J. -M.; Recio-Blanco, A.; Robin, ...

Research on electoral events in conjunction with social media provides opportunities to describe an interesting phenomenon that can be analyzed using sentiment analysis techniques. The goal of the study is to analyze the support of political parties during electoral periods from Twitter comments, including 250 000 tweets regarding the Spanish general elections of 2015 and 2016, respectively. Text mining and natural language processing techniques enable information analysis, and the methodology emphasizes good practices for large-scale data collection retrieved from Twitter through a quantitative analysis of text collection written in the Spanish language. After information extraction obtained in three Spanish regions defined by geolocation, as well as feature selection based on keywords of the main four political parties, we conducted an in-depth examination of Twitter users' support during the course of the election. By weighting the tendency of tweets, we were able to obtain a proposed indicator of support: the positiveness ratio (PR). The results suggest that PR is a feasible barometer to demonstrate the measurable patterns of support tendency regarding political parties and users' behavioral activity to track their affinity on Twitter. The findings indicate consistent support behavior by users toward traditional parties and optimistic users' behavior regarding emerging political parties.

This work has financially been supported by: the Algerian Centre de Recherche en Astronomie, Astrophysique et Géophysique of Bouzareah Observatory; the Austrian FWF Hertha Firnberg Programme through grants T359, P20046, and P23737; the BeLgian federal Science Policy Office (BELSPO) through various Programme de Développement d'Expériences scientifiques (PRODEX) grants; the Brazil-France exchange programmes FAPESP-COFECUB and CAPES-COFECUB; the Chinese National Science Foundation through grant NSFC 11573054; the Czech-Republic Ministry of Education, Youth, and Sports through grant LG 15010; the Danish Ministry of Science; the Estonian Ministry of Education and Research through grant IUT40-1; the European Commission's Sixth Framework Programme through the European Leadership in Space Astrometry (ELSA) Marie Curie Research Training Network (MRTN-CT-2006-033481), through Marie Curie project PIOF-GA-2009-255267 (SAS-RRL), and through a Marie Curie Transfer-of-Knowledge (ToK) fellowship (MTKD-CT-2004-014188); the European Commission's Seventh Framework Programme through grant FP7-606740 (FP7-SPACE-2013-1) for the Gaia European Network for Improved data User Services (GENIUS) and through grant 264895 for the Gaia Research for European Astronomy Training (GREAT-ITN) network; the European Research Council (ERC) through grant 320360 and through the European Union's Horizon 2020 research and innovation programme through grant agreement 670519 (Mixing and Angular Momentum tranSport of massIvE stars – MAMSIE); the European Science Foundation (ESF), in the framework of the Gaia Research for European Astronomy Training Research Network Programme (GREATESF); the European Space Agency in the framework of the Gaia project; the European Space Agency Plan for European Cooperating States (PECS) programme through grants for Slovenia; the Czech Space Office through ESA PECS contract 98058; the Academy of Finland; the Magnus Ehrnrooth Foundation; the French Centre National de la Recherche Scientifique (CNRS) throughm action "Défi ...