DATA MINING OF NIGERIANS’SENTIMENTS ON THE ADMINISTRATION OF FEDERAL GOVERNMENT OF NIGERIA
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
Computers and Applied Computer Science Faculty in "Tibiscus" University of Timişoara, Romania.
Abstract
The opinions and sentiments expressed by
citizens of a country on the policies of the government of
such country are very vital to the overall running of the
affairs of such a government. This paper therefore explored
data mining tools to evaluate peoples’ sentiments (positive
or negative) towards the administration of the Federal
Government of Nigeria (FGN) under President
Muhammadu Buhari (PMB). Data were collected through
a popular social medial network (Twitter) on various tweets
by Nigerians with respect to their perceptions about the
current administration PMB. The simple but powerful
Naïve Bayes (NB) classifier was adopted to classify the
various tweets submitted by Nigerians through this medium
into positive and negative sentiments. For polarity, it was
trained on the combination of Janyce Wiebe’s subjectivity
lexicon and Bing Liu’s subjectivity lexicon which polarized
the submitted words as being negative or positive. Out of
about 13,000 features (peoples’ sentiments) considered,
4,770 of them were used after data cleaning. The results
showed that the proportion of positive and negative
sentiments, as obtained from the data, were 45.2% and
54.8% respectively. However, the data were randomly
partitioned into 80:20 training and testing parts respectively
and the NB classifier was learned on the training set while
its goodness was assessed on the test set. The prediction
accuracy, misclassification error rate, sensitivity and
specificity of the classifier were 78.3%, 21.7%, 82.5% and
88.1% respectively. All analyses were carried out in the
environment of R statistical package (version 3.2.2).
Description
Keywords
Machine Learning, Data Mining, Text mining
Citation
10. Amusa, L. B., Yahaya, W. B., & Balogun, A. O. (2016): Data Mining of Nigerians’ Sentiments on the Contemporary Administration. Annals Computer Science Series 14th Tome 2nd , Fascicle- 2016 Paper 14-2-11. 69-76.