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Twitter user multiclass classification during US 2020 electoral campaign



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Mrzic, Erol (2021) Twitter user multiclass classification during US 2020 electoral campaign. [Trabajo Fin de Máster]

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Due to the unprecedented rise of data content on social media over the last decade, an opportunity for data-based analysis has become a norm in the modern world. Implementing Machine Learning algorithms and Data Science methods virtually every industry changed. One of the most active researching areas in Machine Learning today is Natural Language Processing (NLP), a field of Artificial Intelligence (AI) that allows computers to read, understand, and deduce meaning from human languages. In this paper we applied Natural Language Processing methods and algorithms on two Twitter datasets collected during the US 2020 elections in order to group both users and tweets in multiple categories based on their support for the candidate. The purpose of this work was to establish the possibility to correctly classify these individuals and their individual tweets based on their aggregated opinions and to create a predictive classification model focusing on text analysis. As a result, we constructed, trained and tested multiple models that can help predict the probability of the user’s sentiment toward the candidates based on their tweets. We showed that in 63 % of the cases, we can present high probability of a user’s sentiment classification, according to the amalgamation of their tweets.

Item Type:Trabajo Fin de Máster
Director email
Gómez González, Daniel
Caballero Roldán, Rafael
Robles Morales, José Manuel
Uncontrolled Keywords:Data Science; Machine Learning; Sentiment analysis; Multiclass prediction; Natural Language Processing;
Subjects:Sciences > Statistics
Sciences > Statistics > Social sciences research
Título del Máster:Máster en Minería de Datos e Inteligencia de Negocios. Facultad de Estudios Estadísticos. Curso 2020-2021
ID Code:76575
Deposited On:08 Feb 2023 13:55
Last Modified:08 Feb 2023 13:55

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