02817ntm a2200349 i 4500952012800000999001900128003000800147005001700155006001900172007000300191008004100194020003500235040002500270090002300295100004500318245011500363264003500478264000900513300006300522336002100585337002500606338002300631347002400654500003700678502010000715504003900815520146500854610005202319650004502371650003802416942001302454  00102lcc4070a10000b10000d2024-05-30l0oPSM .F38 2023 r BcpT000003180r2024-05-30 00:00:00w2024-05-30yPSMzCD13582  c100868d100874MY-KuUP20251125110901.0t||||fr|||| 000 0 ta240530t20232023my a|||| |||| 00| 0 eng d  aTHE0009862   (Local)qHardback  aUMPSAbengcUMPerda  aPSM .F38 2023 r Bc1 aFatin Farhanah Binti Abd Rahim,eauthor.10aSentiment analysis on food reviews in kuantan, pahang using machine learning /bFatin Farhanah Binti Abd Rahim 1aKuantan, Pahang:bUMPSA,c2023 4c2023  axiii,  77 pages :bIllustration (some colour) ;e1 CD-ROM.  2rdacontentatext  2rdamediaaunmediated  2rdacarrieravolume  2rdaatext filebPDF  aCenter for Mathematical Sciences  aBachelor of Applied Science in Data Analytics with Honours--Universiti Malaysia Pahang – 2023  aIncludes bibliographical reference3 aCustomer 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 tastes20aCenter for Mathematical SciencesxDissertations 0aUniversities and collegesxDissertations 0aFinal Year ProjectxDissertations  2lcccPSM