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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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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. |
format | Online Article Text |
id | pubmed-10280231 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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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