Sentiment analysis on food reviews in kuantan, pahang using machine learning
Customer sentiment analysis is an automated way detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. Social media, blogs and websites have become cha...
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| Format: | Undergraduates Project Papers |
| Language: | en |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/46452/1/Sentiment%20analysis%20on%20food%20reviews%20in%20kuantan%2C%20pahang%20using%20machine%20learning.pdf https://umpir.ump.edu.my/id/eprint/46452/ |
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| Summary: | Customer sentiment analysis is an automated way detecting sentiments in online interactions in order to assess customer opinions about a product, brand or service. It assists companies in gaining insights and efficiently responding to their customers. Social media, blogs and websites have become channels for people to voice their opinions openly on a variety discussion topics making it ideal domain to utilise customers sentiment analysis. This study presents a machine learning approach to analyse how sentiment analysis detects positive and negative feedback about food reviews in Kuantan, Pahang. Customer feedback data will be taken from the suitable social media. All of these data processes will use Python via Jupyter Notebook. These data will be go through a pre-processing stage and the data will be fed to two supervised learning algorithms consisting of Support Vector Machine (SVM) and Logistic Regression. The performance of each model will be compared to select the most accurate model. At the end, the objectives of the project can be achieved because the sentiment analysis is necessary to collect and analyse the large amount of data to give benefit to the customers and producer to discover a psotive or negative reviews. Among the benefits is that the restaurant can make such improvements such as can upgrade existing menu based on customers feedback and customers can choose which is the best food that suits their tastes |
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