Machine Learning applications in elections. Sentimental analysis of tweets during campaigns

Session

Computer Science and Communication Engineering

Description

Machine Learning is one of the most impactful technology of our era. Increasingly powerful computers harnessed to algorithms refined over the past decade are driving an explosion of applications in everything from medical physics to materials. In this paper we are going to present how to use machine learning to analyse election campaigns. Our focus is on using natural language processing, especially sentimental analysis and text classification to analyse public posts of running candidates. As a case study, we will use a dataset of public tweets written in the English language, by a group of selected politicians. We will classify these tweets in three categories: Positive, Neutral and Negative by using classification algorithms. Since we are experiencing government elections in our country we will analyse how the sentiment of tweets by candidates is changing before and after elections. We will also analyse the correlation between the sentiments of tweets with the reaction they get from social media. The purpose of the presentation is to encourage social scientists to start using machine learning as an important tool for analysing election campaigns and their impact on public presentation of politicians.

Keywords:

machine learning, sentimental analysis, elections

Session Chair

Bertan Karahoda

Session Co-Chair

Krenare Pireva

Proceedings Editor

Edmond Hajrizi

ISBN

978-9951-550-19-2

Location

Pristina, Kosovo

Start Date

26-10-2019 1:30 PM

End Date

26-10-2019 3:00 PM

DOI

10.33107/ubt-ic.2019.266

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Oct 26th, 1:30 PM Oct 26th, 3:00 PM

Machine Learning applications in elections. Sentimental analysis of tweets during campaigns

Pristina, Kosovo

Machine Learning is one of the most impactful technology of our era. Increasingly powerful computers harnessed to algorithms refined over the past decade are driving an explosion of applications in everything from medical physics to materials. In this paper we are going to present how to use machine learning to analyse election campaigns. Our focus is on using natural language processing, especially sentimental analysis and text classification to analyse public posts of running candidates. As a case study, we will use a dataset of public tweets written in the English language, by a group of selected politicians. We will classify these tweets in three categories: Positive, Neutral and Negative by using classification algorithms. Since we are experiencing government elections in our country we will analyse how the sentiment of tweets by candidates is changing before and after elections. We will also analyse the correlation between the sentiments of tweets with the reaction they get from social media. The purpose of the presentation is to encourage social scientists to start using machine learning as an important tool for analysing election campaigns and their impact on public presentation of politicians.