  On December 7, 2020, Ghanaians participated in the polls to determine their
president for the next four years. To gain insights from this presidential
election, we conducted stance analysis (which is not always equivalent to
sentiment analysis) to understand how Twitter, a popular social media platform,
reflected the opinions of its users regarding the two main presidential
candidates. We collected a total of 99,356 tweets using the Twitter API
(Tweepy) and manually annotated 3,090 tweets into three classes: Against,
Neutral, and Support. We then performed preprocessing on the tweets. The
resulting dataset was evaluated using two lexicon-based approaches, VADER and
TextBlob, as well as five supervised machine learning-based approaches: Support
Vector Machine (SVM), Logistic Regression (LR), Multinomial Na\"ive Bayes
(MNB), Stochastic Gradient Descent (SGD), and Random Forest (RF), based on
metrics such as accuracy, precision, recall, and F1-score. The best performance
was achieved by Logistic Regression with an accuracy of 71.13%. We utilized
Logistic Regression to classify all the extracted tweets and subsequently
conducted an analysis and discussion of the results. For access to our data and
code, please visit:
https://github.com/ShesterG/Stance-Detection-Ghana-2020-Elections.git
