What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

Muhammad Mujahid; Furqan Rustam; Fahad Alasim; MuhammadAbubakar Siddique; Imran Ashraf

doi:10.7717/peerj-cs.1193

What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets

PeerJ Comput Sci. 2023 Jan 13:9:e1193. doi: 10.7717/peerj-cs.1193. eCollection 2023.

Authors

Muhammad Mujahid¹, Furqan Rustam², Fahad Alasim³, MuhammadAbubakar Siddique⁴, Imran Ashraf⁵

Affiliations

¹ Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Raheem Yar Khan, Pakistan.
² School of Computer Science, University College Dublin, Dublin, Ireland.
³ Department of Industrial Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia.
⁴ Department of Computer Science and Information Technology, Ghazi University, Dera Ghazi Khan, Punjab, Pakistan.
⁵ Information and Communication Engineering, Yeungnam University, Gyeongsan si, South Korea.

Abstract

With the rise of social media platforms, sharing reviews has become a social norm in today's modern society. People check customer views on social networking sites about different fast food restaurants and food items before visiting the restaurants and ordering food. Restaurants can compete to better the quality of their offered items or services by carefully analyzing the feedback provided by customers. People tend to visit restaurants with a higher number of positive reviews. Accordingly, manually collecting feedback from customers for every product is a labor-intensive process; the same is true for sentiment analysis. To overcome this, we use sentiment analysis, which automatically extracts meaningful information from the data. Existing studies predominantly focus on machine learning models. As a consequence, the performance analysis of deep learning models is neglected primarily and of the deep ensemble models especially. To this end, this study adopts several deep ensemble models including Bi long short-term memory and gated recurrent unit (BiLSTM+GRU), LSTM+GRU, GRU+recurrent neural network (GRU+RNN), and BiLSTM+RNN models using self-collected unstructured tweets. The performance of lexicon-based methods is compared with deep ensemble models for sentiment classification. In addition, the study makes use of Latent Dirichlet Allocation (LDA) modeling for topic analysis. For experiments, the tweets for the top five fast food serving companies are collected which include KFC, Pizza Hut, McDonald's, Burger King, and Subway. Experimental results reveal that deep ensemble models yield better results than the lexicon-based approach and BiLSTM+GRU obtains the highest accuracy of 95.31% for three class problems. Topic modeling indicates that the highest number of negative sentiments are represented for Subway restaurants with high-intensity negative words. The majority of the people (49%) remain neutral regarding the choice of fast food, 31% seem to like fast food while the rest (20%) dislike fast food.

Keywords: Ensemble model; Latent Dirichlet Allocation; Sentiment analysis; TextBlob.

Grants and funding

The authors receieved no funding for this work.