03798ntm a2200301 i 4500003000800000005001700008006001900025007000300044008004100047020003300088040002500121100004700146245014300193264003500336264001100371300006100382336002100443337002500464338002300489347002400512500003700536502010000573504003900673520266400712610005203376650004503428650002303473MY-KuUP20251125110901.0a||||fr|||| 00| 0 ta240530t20232023my a|||fs|||| 000 0 eng d  aTHE0009866 (Local)qHardback  aUMPSAbengcUMPerda1 aQiryn Adriana Binti Kharul Zaman,eauthor.10aPredictive analytics for the  sentiment of malaysian place of  interest using machine learning  models /cQiryn Adriana Binti Kharul Zaman 1aKuantan, Pahang:bUMPSA,c2023 4c©2023  axiv, 92 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 aSentiment analysis is a method of automatically identifying sentiments expressed  in online interactions with the aim of evaluating users or customers' opinions on a  product, brand, or service. It helps companies gain valuable insights and respond to their  customers more efficiently. As people use social media, forums, blogs, and the web to  express their opinions on various discussion topics, these channels have become an ideal  domain for utilizing customer sentiment analysis. The focus of this study is to conduct  Natural Language Processing (NLP) on tweets and make a better classification of  sentiment using Malaya. Furthermore, this study also trains three machine learning  algorithms to predict the sentiment of textual data. Moreover, this study also creates  dashboard to visualize social media insights and suggest recommendations based on the  insights. The study gathered users or customers feedback from Twitter on 1st January  2023 to 1st March 2023 using the social media monitoring software, Determ, which  retrieves tweets in real-time based on search terms, time, users, and likes. The tweets  containing feedbacks and responses were organized into tables and saved as a CSV file.  Subsequently, the study proceeded with the pre-processing stage to handle missing and  erroneous values in the data. Additionally, several Natural Language Processing (NLP)  techniques were employed to pre-process the text data, as part of the machine learning  process. The data was then divided into training and testing sets, and was trained using  three different supervised learning algorithms, namely Support Vector Machine, Random  Forest, and Naive Bayes. Finally, the performance of prediction of each model was  compared to identify the most accurate one, and based on the analysis, it was concluded  that Support Vector Machine exhibited the best performance in terms of accuracy, recall  score, F1 score, and precision score. Furthermore, it is worth noting that this sentiment  analysis research is extended to analyze sentiments expressed in texts written in Malay  language by utilizing the Natural Language-Toolkit library for Bahasa Malaysia, powered  by Tensorflow and PyTorch. Regarding the outcomes of the customer sentiment analysis,  there are some recommendations that can be adopted to enhance the effectiveness of the  study. For instance, the analysis can be extended to include customer feedback data  collected from social media platforms such as Facebook, Instagram, Tik Tok, web and  forums. Additionally, the performance of the customer sentiment analysis model can be  improved by leveraging deep learning techniques.20aCenter for Mathematical SciencesxDissertations 0aUniversities and collegesxDissertations 0aFinal Year Project