Sentiment Analysis of Fast Food Companies With Deep Learning Models

Abstract

In the modern era, Internet usage has become a basic necessity in the lives of people. Nowadays, people can perform online shopping and check the customer's views about products that purchased online. Social networking services enable users to post opinions on public platforms. Analyzing people's opinions helps corporations to improve the quality of products and provide better customer service. However, analyzing this content manually is a daunting task. Therefore, we implemented sentiment analysis to make the process automatically. The entire process includes data collection, pre-processing, word embedding, sentiment detection and classification using deep learning techniques. Twitter was chosen as the source of data collection and tweets collected automatically by using Tweepy. In this paper, three deep learning techniques were implemented, which are CNN, Bi-LSTM and CNN-Bi-LSTM. Each of the models trained on three datasets consists of 50K, 100K and 200K tweets. The experimental result revealed that, with the increasing amount of training data size, the performance of the models improved, especially the performance of the Bi-LSTM model. When the model trained on the 200K dataset, it achieved about 3% higher accuracy than the 100K dataset and achieved about 7% higher accuracy than the 50K dataset. Finally, the Bi-LSTM model scored the highest performance in all metrics and achieved an accuracy of 95.35%.