InsightVista: Unveiling visitor sentiments and trends for Terengganu state museum using text analytics

Nurul Husna Tarmizi; Nur Iman Husna A’riff; Norulhidayah Isa; Sarah Yusoff; Nor Hasnul Azirah Abdul Hamid

doi:10.21834/e-bpj.v10iSI31.6938

Authors

Nurul Husna Tarmizi College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Cawangan Terenggnu, Kampus Kuala Terengganu, Terengganu, Malaysia
Nur Iman Husna A’riff College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Cawangan Terenggnu, Kampus Kuala Terengganu, Terengganu, Malaysia.
Norulhidayah Isa College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Cawangan Terenggnu, Kampus Kuala Terengganu, Terengganu, Malaysia.
Sarah Yusoff College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Cawangan Terenggnu, Kampus Kuala Terengganu, Terengganu, Malaysia.
Nor Hasnul Azirah Abdul Hamid College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, Cawangan Terenggnu, Kampus Kuala Terengganu, Terengganu, Malaysia.

DOI:

https://doi.org/10.21834/e-bpj.v10iSI31.6938

Keywords:

sentiment analysis, clustering, visitor experiences, CRISP-DM

Abstract

This project explores visitor experiences by conducting sentiment analysis and clustering analysis on 1,973 visitor reviews posted on Google Maps and TripAdvisor. Utilizing machine learning algorithms like Support Vector Machine (SVM) and the K-Means algorithm, the project achieved 87.66% accuracy in sentiment classification and identified three optimal clusters. Then, a detailed analysis of both results were conducted. The insights are visualized through an interactive dashboard, enabling museum management to make informed decisions. Despite data limitations, the project offers strategic recommendations for ongoing improvement, aiming to enhance visitor satisfaction and engagement at the Terengganu State Museum.

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