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Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble

Muhammad Rizwan1,2, Muhammad Faheem Mushtaq1, Maryam Rafiq2, Arif Mehmood3, Isabel de la Torre Diez4, Monica Gracia Villar5,6,7, Helena Garay5,8,9, Imran Ashraf10,*

1 Department of Artificial Intelligence, The Islamia University of Bahawalpur, Bahawalnagar, 63100, Pakistan
2 Department of Information Technology, Khwaja Fareed University of Engineering and IT, RYKhan, 64200, Pakistan
3 Department of Computer Science & Information Technology, The Islamia University of Bahawalpur, Bahawalnagar, 63100, Pakistan
4 Department of Signal Theory and Communications and Telematic Engineering, Unviersity of Valladolid, Paseo de Belén 15, Valladolid, 47011, Spain
5 Faculty of Social Science and Humanities, Universidad Europea del Atlántico Isabel Torres 21, Santander, 39011, Spain
6 Department of Project Management, Universidad Internacional Iberoamericana, Campeche, 24560, México
7 Fundación Universitaria Internacional de Colombia, Bogotá, 111611, Colombia
8 Universidad Internacional Iberoamericana, Arecibo, Puerto Rico, 00613, USA
9 Universidade Internacional do Cuanza, Cuito, 46703, Angola
10 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, South Korea

* Corresponding Author: Imran Ashraf. Email: email

TSP_CMC_37347.pdf

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