TWEERIFY: A Web-Based Sentiment Analysis System Using Rule and Deep Learning Techniques
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Date
2022-03
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Journal ISSN
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Publisher
Springer
Abstract
Social media platforms have ceased to be a platform for interaction and
entertainment alone, and they have become a platform where citizens express their
opinions about issues that affect them. In recent years, it has become a powerful plat-
form where elections are won and lost. Therefore, organizations and governments
are increasingly interested in citizen’s views expressed on social media platforms.
This research presents a novel approach to carry out aspect-level sentiment anal-
ysis of users’ tweets using rule and convolutional neural network (CNN)-based deep
learning technique. The rule-based technique was used to detect and extract senti-
ments from preprocessed tweets, while the CNN-based deep learning technique was
employed for the sentiment polarity classification. A total of 26,378 tweets collected
using “security” and “Nigeria” keywords were used to test the proposed model. The
proposed model outperformed existing state-of-the-arts GloVe and word2vec models
with an accuracy of 82.31%, recall value of 82.21%, precision value of 82.75% and
F1 score of 81.89%. The better performance of the proposed techniques could be
as a result of the rule-based techniques that was introduced to capture sentiments
expressed in slangs or informal languages which GloVe and word2vec have not been
designed to capture