@article{(Open Science Index):https://publications.waset.org/pdf/10009499,
	  title     = {Real Time Classification of Political Tendency of Twitter Spanish Users based on Sentiment Analysis},
	  author    = {Marc Solé and  Francesc Giné and  Magda Valls and  Nina Bijedic},
	  country	= {},
	  institution	= {},
	  abstract     = {What people say on social media has turned into a
rich source of information to understand social behavior. Specifically,
the growing use of Twitter social media for political communication
has arisen high opportunities to know the opinion of large numbers
of politically active individuals in real time and predict the global
political tendencies of a specific country. It has led to an increasing
body of research on this topic. The majority of these studies have
been focused on polarized political contexts characterized by only
two alternatives. Unlike them, this paper tackles the challenge
of forecasting Spanish political trends, characterized by multiple
political parties, by means of analyzing the Twitters Users political
tendency. According to this, a new strategy, named Tweets Analysis
Strategy (TAS), is proposed. This is based on analyzing the users
tweets by means of discovering its sentiment (positive, negative or
neutral) and classifying them according to the political party they
support. From this individual political tendency, the global political
prediction for each political party is calculated. In order to do this,
two different strategies for analyzing the sentiment analysis are
proposed: one is based on Positive and Negative words Matching
(PNM) and the second one is based on a Neural Networks Strategy
(NNS). The complete TAS strategy has been performed in a Big-Data
environment. The experimental results presented in this paper reveal
that NNS strategy performs much better than PNM strategy to analyze
the tweet sentiment. In addition, this research analyzes the viability
of the TAS strategy to obtain the global trend in a political context
make up by multiple parties with an error lower than 23%.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {12},
	  number    = {9},
	  year      = {2018},
	  pages     = {697 - 706},
	  ee        = {https://publications.waset.org/pdf/10009499},
	  url   	= {https://publications.waset.org/vol/141},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 141, 2018},
	}