%0 Journal Article
%T Machine Learning Approaches for Classifying the Distribution of Covid-19 Sentiments
%A M. Kuyo
%A S. Mwalili
%A E. Okang¡¯o
%J Open Journal of Statistics
%P 620-632
%@ 2161-7198
%D 2021
%I Scientific Research Publishing
%R 10.4236/ojs.2021.115037
%X Previously,
rapid disease detection and prevention was difficult. This is because disease
modeling and prediction was dependent on a manually obtained dataset that
includes use of survey. With the increased use of social media platforms like
Twitter, Facebook, Instagram, etc., data mining and sentiment analysis can help
avoid diseases. Sentiment analysis is a powerful tool for analyzing people¡¯s
perceptions, emotions, value assessments, attitudes, and feelings as expressed
in texts. The purpose of this research is to use machine learning techniques to
classify and predict the spatial distribution of positive and negative
sentiments of Covid-19 pandemic. This study research has employed machine
learning to classify spatial distribution of Covid-19 <span style=\"font-family:Verdana;\">twitter sentiments as positive or negative. The data for this study were
geo-tagged</span><span style=\"font-family:Verdana;\"> tweets concerning COVID-19 which were live streamed using streamR
package. The key terms used for streaming the data were</span><span style=\"font-family:Verdana;\">:</span><span style=\"font-family:Verdana;\"> Corona, Covid-19, sanitizer, virus, lockdown,
quarantine, and social distance. The classification used Naive Bayes algorithms
with ngram approaches. N-Gram model is a probabilistic language model used to
predict next item in a sequence in the form (n</span><span style=\"font-family:;\" \"=\"\">&nbsp;<span style=\"font-family:Verdana;\">-</span></span><span style=\"font-family:;\" \"=\"\">&nbsp;</span><span style=\"font-family:Verdana;\">1) order
Markov. It relies on the Markov assumption¡ªthe probability of a word depends
only on the previous word without looking too far into the past. The steps
followed in this research include</span><span style=\"font-family:Verdana;\">: </span><span style=\"font-family:;\" \"=\"\"><span style=\"font-family:Verdana;\">cleaning and preprocessing the data, text tokenization using n-gram </span><i><span style=\"font-family:Verdana;\">i.e.</span></i><span style=\"font-family:Verdana;\"> 1-gram, 2-gram, and 3-gram, tweets
were converted or weighted into a matrix of numeric vectors using Term
Frequency Inverse-Document. Also, data were divided 80:20 between train and
test data. A confusion matrix was utilized to evaluate the classification
accuracy, precision, and recall performance of the various algorithms tested.
Prediction was done using the best performing Naive Bayes algorithm. The
results of this research showed that under Multinomial Naive Bayes, unigram
accuracy was 92.02%, bigram accuracy was 97.37%, and
%K Machine Learning
%K Sentiment Analysis
%K Natural Language Processing
%K Covid-19
%K Naive Bayes
%K N-Gram
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=112294