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

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 bette...

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Detalles Bibliográficos
Autores principales: Mujahid, Muhammad, Rustam, Furqan, Alasim, Fahad, Siddique, MuhammadAbubakar, Ashraf, Imran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280231/
https://www.ncbi.nlm.nih.gov/pubmed/37346556
http://dx.doi.org/10.7717/peerj-cs.1193
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author Mujahid, Muhammad
Rustam, Furqan
Alasim, Fahad
Siddique, MuhammadAbubakar
Ashraf, Imran
author_facet Mujahid, Muhammad
Rustam, Furqan
Alasim, Fahad
Siddique, MuhammadAbubakar
Ashraf, Imran
author_sort Mujahid, Muhammad
collection PubMed
description 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.
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spelling pubmed-102802312023-06-21 What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets Mujahid, Muhammad Rustam, Furqan Alasim, Fahad Siddique, MuhammadAbubakar Ashraf, Imran PeerJ Comput Sci Data Science 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. PeerJ Inc. 2023-01-13 /pmc/articles/PMC10280231/ /pubmed/37346556 http://dx.doi.org/10.7717/peerj-cs.1193 Text en ©2023 Mujahid et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Data Science
Mujahid, Muhammad
Rustam, Furqan
Alasim, Fahad
Siddique, MuhammadAbubakar
Ashraf, Imran
What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets
title What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets
title_full What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets
title_fullStr What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets
title_full_unstemmed What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets
title_short What people think about fast food: opinions analysis and LDA modeling on fast food restaurants using unstructured tweets
title_sort what people think about fast food: opinions analysis and lda modeling on fast food restaurants using unstructured tweets
topic Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280231/
https://www.ncbi.nlm.nih.gov/pubmed/37346556
http://dx.doi.org/10.7717/peerj-cs.1193
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