TWEERIFY: A Web-Based Sentiment Analysis System Using Rule and Deep Learning Techniques

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 platform 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 analysis of users� tweets using rule and convolutional neural network (CNN)-based deep learning technique. The rule-based technique was used to detect and extract sentiments 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. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.