Villanueva Vega, Danny Gilberto

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Finding similar tweets within health related topics
(2019-07-10) Villanueva Vega, Danny Gilberto; Rodríguez-Martínez, Manuel; College of Engineering; Rivera-Gallego, Wilson; Rivera-Vega, Pedro I.; Department of Electrical and Computer Engineering; Cruzado-Vélez, Ivette
Social networks have become a very important means to facilitate the creation and sharing of information, ideas, news, and opinions on many topics. They also provide real-time information on sales, marketing, politics, natural disasters, and crisis situations, among others. These networks include Facebook, Twitter, WhatsApp, and Instagram, to name a few. In this work, we shall focus our eﬀorts on the Twitter social network. This network provides a mechanism for people to express their views using short messages (i.e., 280 characters) called tweets. In this project, we investigate and implement text similarity neural network models in such a way that we can: 1) know if they are related or not with a disease, 2) group similar tweets to those that we have already captured, analyzed or stored, and 3) ﬁnd similarity index between tweets using diﬀerent learning algorithms. We based our work on, semantic similarity approaches and text similarity measures using Deep Learning algorithms to deliver reliable information about health-related topics.