Smart Customer Care: Scraping Social Media to Predict Customer Satisfaction in Egypt Using Machine Learning Models

This paper proposes the utilization of posts from social media to extract and analyze customer opinions and sentiments towards any specified topic in Egypt. Summarized statistics and sentiment values are then displayed to the consumer (companies such as Vodafone, WE etc..) through both an attractive and functional user interface. Text, location, and time of thousands of posts are scrapped, stored, preprocessed, then managed through topic modelling to infer all the hidden themes and delivered to a Recurrent Neural Network (RNN) to output whether the topic was positive or negative. Topic modelling was implemented using the BERT architecture and AraBert word embedding. Sentiment analysis model training was conducted on approximately 4000 rows of processed data and made use of Arabic glove embedding to speed up sentiment and word pattern recognition. Five models were experimented on: LSTM, GRU, CNN, LSTM + CNN and GRU + CNN. Overall, the GRU was the model with the best results, concluding with an accuracy of (86.19%), loss of (0.3349) and an F1-score of (0.858) when validating through the test data.