A dissertation submitted to the Faculty of Science,
University of the Witwatersrand, Johannesburg,
in fulfilment of the requirements for the degree of Master of Science., University of the Witwatersrand, Johannesburg, 2018 / Social network sites such as Twitter have created vibrant and diverse communities
in which users express their opinions and views on a variety of topics such as politics.
Extensive research has been conducted in countries such as Ireland, Germany
and the United States, in which text mining techniques have been used to obtain
information from politically oriented tweets. The purpose of this research was
to determine if text mining techniques can be used to uncover meaningful information
from a corpus of political tweets collected during the 2014 South African
General Election. The Twitter Application Programming Interface was used to
collect tweets that were related to the three major political parties in South Africa,
namely: the African National Congress (ANC), the Democratic Alliance (DA) and
the Economic Freedom Fighters (EFF). The text mining techniques used in this research
are: sentiment analysis, clustering, association rule mining and word cloud
analysis. In addition, a correlation analysis was performed to determine if there exists
a relationship between the total number of tweets mentioning a political party
and the total number of votes obtained by that party. The VADER (Valence Aware
Dictionary for sEntiment Reasoning) sentiment classifier was used to determine
the public’s sentiment towards the three main political parties. This revealed an
overwhelming neutral sentiment of the public towards the ANC, DA and EFF. The
result produced by the VADER sentiment classifier was significantly greater than
any of the baselines in this research. The K-Means cluster algorithm was used
to successfully cluster the corpus of political tweets into political-party clusters.
Clusters containing tweets relating to the ANC and EFF were formed. However,
tweets relating to the DA were scattered across multiple clusters. A fairly strong
relationship was discovered between the number of positive tweets that mention
the ANC and the number of votes the ANC received in election. Due to the lack of
data, no conclusions could be made for the DA or the EFF. The apriori algorithm
uncovered numerous association rules, some of which were found to be interest-
ing. The results have also demonstrated the usefulness of word cloud analysis in
providing easy-to-understand information from the tweet corpus used in this study.
This research has highlighted the many ways in which text mining techniques can
be used to obtain meaningful information from a corpus of political tweets. This
case study can be seen as a contribution to a research effort that seeks to unlock the
information contained in textual data from social network sites. / MT 2018
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:wits/oai:wiredspace.wits.ac.za:10539/25757 |
| Date | January 2018 |
| Creators | Singh, Asheen |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | Online resource (173 leaves), application/pdf |
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