Sentiment Analysis of the Sheikh Zayed Grand Mosque’s Visitor Reviews on Google Maps Using the VADER Method
DOI:
https://doi.org/10.59395/ijadis.v5i1.1320Keywords:
Lexicon, Opinion Mining, Religious Tourism, Social Network Analysis, Text Classification, VADERAbstract
The Sheikh Zayed Grand Mosque in Solo is a replica of the Zayed Grand Mosque in Abu Dhabi. Many people have provided reviews on Google Maps after visiting the mosque. This research aims to determine the sentiment results regarding visitors’ reviews by developing a sentiment analysis model using a combination of the Valance Aware Dictionary for Sentiment Reasoning (VADER) and Deep Translator methods. This research was conducted in two phases. The first phase proposed a sentiment analysis model using VADER and Deep-Translator with public datasets. Later, the resulting sentiment analysis model was applied in the second phase to analyze the dataset of mosque visitor reviews and determine public perceptions. This research compares two preprocessing models (PPTV1 and PPTV2) and continues with the translation and sentiment prediction processes. The evaluation results show the proposed model (PPTV2) achieved the best average accuracy values of 72%, precision of 83%, recall of 72%, and F1-Score of 75% for the three examined datasets. The results of visitor review sentiment obtained showed 83.3% positive, 9.5% neutral, and 7.2% negative. The analysis findings show that people are amazed by the beauty and majesty of the mosque. However, some people provide negative reviews of the mosque’s facilities.
Downloads
References
A. E. Subhan, E. Dan, E. Wisata, S. Masjid, R. Sheikh, and Z. Solo, "Eksistensi Dan Efektivitas Wisata Syariah Masjid Raya Sheikh Zayed Solo," vol. 7, no. 1, pp. 42-55, 2024, doi: 10.31943/afkarjournal.v7i1.877.
Samsir, "Analisis Sentimen Pembelajaran Daring Pada Twitter di Masa Pandemi COVID-19 Menggunakan Metode Naïve Bayes," J. Media Inform. Budidarma, vol. 5, no. 1, pp. 157-163, 2021, doi: 10.30865/mib.v5i1.2604. https://doi.org/10.30865/mib.v5i1.2604
N. S. Fathullah, "Analisis Sentimen Terhadap Rating dan Ulasan Film dengan menggunakan Metode Klasifikasi Naïve BayesdenganFitur Lexicon-Based," J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol. 4, no. 2, pp. 590-593, 2020.
F. Fazrin, N. O. Pratiwi, and R. Andreswari, "Perbandingan Algoritma K-Nearest Neighbor dan Logistic Regression pada Analisis Sentimen terhadap Vaksinasi Covid-19 pada Media Sosial Twitter dengan Pelabelan Vader dan Textblob," J. e-Proceeding Eng., vol. 10, no. 2, pp. 1596-1604, 2023.
W. Medhat, A. Hassan, and H. Korashy, "Sentiment analysis algorithms and applications: A survey," Ain Shams Eng. J., vol. 5, no. 4, pp. 1093-1113, 2014, doi: 10.1016/j.asej.2014.04.011. https://doi.org/10.1016/j.asej.2014.04.011
Y. Qi and Z. Shabrina, "Sentiment analysis using Twitter data: a comparative application of lexicon- and machine-learning-based approach," Soc. Netw. Anal. Min., vol. 13, no. 1, p. 31, Feb. 2023, doi: 10.1007/s13278-023-01030-x. https://doi.org/10.1007/s13278-023-01030-x
R. Bose, P. S. Aithal, and S. Roy, "Survey of Twitter Viewpoint on Application of Drugs by VADER Sentiment Analysis among Distinct Countries," Int. J. Manag. Technol. Soc. Sci., no. March 2021, pp. 110-127, 2021, doi: 10.47992/ijmts.2581.6012.0132.
https://doi.org/10.47992/IJMTS.2581.6012.0132
A. J. Nair, G. Veena, and A. Vinayak, "Comparative study of Twitter Sentiment on COVID - 19 Tweets," Proc. - 5th Int. Conf. Comput. Methodol. Commun. ICCMC 2021, pp. 1773-1778, 2021, doi: 10.1109/ICCMC51019.2021.9418320. https://doi.org/10.1109/ICCMC51019.2021.9418320
E. M. Baesa, E. R. Raro, V. Q. Parillas, J. M. Berina, and T. D. Palaoag, "Sentiment Analysis of Hog Raisers during African Swine Fever using Vader Lexicon-Based Methods," Proc. 2022 IEEE 7th Int. Conf. Inf. Technol. Digit. Appl., 2022, doi: 10.1109/ICITDA55840.2022.9971165. https://doi.org/10.1109/ICITDA55840.2022.9971165
A. G. Budianto, B. Wirjodirdjo, I. Maflahah, and D. Kurnianingtyas, "Sentiment Analysis Model for KlikIndomaret Android App During Pandemic Using Vader and Transformers NLTK Library," IEEE Int. Conf. Ind. Eng. Eng. Manag., vol. 2022-Decem, pp. 423-427, 2022, doi: 10.1109/IEEM55944.2022.9989577. https://doi.org/10.1109/IEEM55944.2022.9989577
P. A. Sumitro, Rasiban, D. I. Mulyana, and W. Saputro, "Analisis Sentimen Terhadap Vaksin Covid-19 di Indonesia pada Twitter Menggunakan Metode Lexicon Based," J-ICOM - J. Inform. dan Teknol. Komput., vol. 2, no. 2, pp. 50-56, 2021, doi: 10.33059/j-icom.v2i2.4009. https://doi.org/10.33059/j-icom.v2i2.4009
R. Arief and K. Imanuel, "Analisis Sentimen Topik Viral Desa Penari pada Media Sosial Twitter dengan Metode Lexicon Based," J. Ilm. MATRIK, vol. 21, no. 3, pp. 242-250, 2019. https://doi.org/10.33557/jurnalmatrik.v21i3.727
K. Barik and S. Misra, "Analysis of customer reviews with an improved VADER lexicon classifier," J. Big Data, vol. 11, no. 1, p. 10, Jan. 2024, doi: 10.1186/s40537-023-00861-x. https://doi.org/10.1186/s40537-023-00861-x
F. Paian Sitorus, E. Utami, and M. Parwanto Kurniawan, "Public Sentiment Analysis about Independent Curriculum with VADER Annotations using the Naive Bayes and K-Nearest Neighbor Methods," Int. J. Innov. Sci. Res. Technol., vol. 8, no. 8, 2023, doi: 10.5281/zenodo.8310746.
Y. Asri and M. Fajri, "Sentiment Analysis of PLN Mobile Review Data Using Lexicon Vader and Naive Bayes Classification," in 2023 International Conference on Networking, Electrical Engineering, Computer Science, and Technology (IConNECT), Aug. 2023, pp. 132-137. doi: 10.1109/IConNECT56593.2023.10327064. https://doi.org/10.1109/IConNECT56593.2023.10327064
V. Bonta, N. Kumaresh, and N. Janardhan, "A Comprehensive Study on Lexicon Based Approaches for Sentiment Analysis," Asian J. Comput. Sci. Technol., vol. 8, no. S2, pp. 1-6, 2019, doi: 10.51983/ajcst-2019.8.s2.2037. https://doi.org/10.51983/ajcst-2019.8.S2.2037
H. Yuliansyah, S. A. Mulasari, S. Sulistyawati, F. A. Ghozali, and B. Sudarsono, "Sentiment Analysis of the Waste Problem based on YouTube comments using VADER and Deep Translator," J. Media Inform. Budidarma, vol. 8, pp. 663-673, 2024, doi: 10.30865/mib.v8i1.6918.
M. Fahmi, S. Hidayat, and A. F. Hidayatullah, "Application of Lexicon Based for Sentiment Analysis of Covid-19 Booster Vaccinations on Twitter Social Media Using Naïve Bayes Method," J. Tek. Inform., vol. 3, no. 4, pp. 1119-1124, 2022, doi: 10.20884/1.jutif.2022.3.4.565. https://doi.org/10.20884/1.jutif.2022.3.4.565
M. R. Ningsih, K. A. H. Wibowo, A. U. Dullah, and J. Jumanto, "Global recession sentiment analysis utilizing VADER and ensemble learning method with word embedding," J. Soft Comput. Explor., vol. 4, no. 3, pp. 142-151, 2023, doi: 10.52465/joscex.v4i3.193. https://doi.org/10.52465/joscex.v4i3.193
C. Hutto and E. Gilbert, "VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text," in Proceedings of the International AAAI Conference on Web and Social Media, May 2014, vol. 8, no. 1, pp. 216-225. doi: 10.1609/icwsm.v8i1.14550. https://doi.org/10.1609/icwsm.v8i1.14550
S. Elbagir and J. Yang, "Twitter sentiment analysis using natural language toolkit and Vader sentiment," in Lecture Notes in Engineering and Computer Science, 2019, vol. 2239, pp. 12-16. https://doi.org/10.1142/9789811215094_0005
W. A. Luqyana, I. Cholissodin, and R. S. Perdana, "Analisis Sentimen Cyberbullying pada Komentar Instagram dengan Metode Klasifikasi Support Vector Machine," J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 2, no. 11, pp. 4704-4713, 2018, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/3051
W. E. Nurjanah, R. S. Perdana, and M. A. Fauzi, "Analisis Sentimen Terhadap Tayangan Televisi Berdasarkan Opini Masyarakat pada Media Sosial Twitter menggunakan Metode K-Nearest Neighbor dan Pembobotan Jumlah Retweet," J. Pengemb. Teknol. Inf. Dan Ilmu Kompute, vol. 1, no. 12, pp. 1750-1757, 2017, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/631
P. Antinasari, R. S. Perdana, and M. A. Fauzi, "Analisis Sentimen Tentang Opini Film Pada Dokumen Twitter Berbahasa Indonesia Menggunakan Naive Bayes Dengan Perbaikan Kata Tidak Baku," J. Pengemb. Teknol. Inf. Dan Ilmu Komput., vol. 1, no. 12, pp. 1733-1741, 2017, [Online]. Available: https://j-ptiik.ub.ac.id/index.php/j-ptiik/article/view/629
C. Prakoso and A. Hermawan, "Perbandingan Model Machine Learning dalam Analisis Sentimen Ulasan Pengunjung Keraton Yogyakarta pada Google Maps," Kaji. Ilm. Inform. dan Komput., vol. 4, no. 3, pp. 1292-1302, 2023, doi: 10.30865/klik.v4i3.1419.
T. Dzulkarnain, D. E. Ratnawati, and B. Rahayudi, "Penggunaan Metode Naïve Bayes Classifier pada Analisis Sentimen Penilaian Masyarakat Terhadap Pelayanan Rumah Sakit di Malang," J. Teknol. Inf. dan Ilmu Komput., vol. 10, no. 7, pp. 1453-1460, 2023, doi: 10.25126/jtiik.1077979. https://doi.org/10.25126/jtiik.1077979
M. Hayaty and A. H. Pratama, "Performance of Lexical Resource and Manual Labeling on Long Short-Term Memory Model for Text Classification," J. Ilm. Tek. Elektro Komput. dan Inform., vol. 9, no. 1, pp. 74-84, 2023, doi: 10.26555/jiteki.v9i1.25375.
A. Ria Devina Endsuy, "Sentiment Analysis between VADER and EDA for the US Presidential Election 2020 on Twitter Datasets," J. Appl. Data Sci., vol. 2, no. 1, pp. 8-18, 2021, doi: 10.47738/jads.v2i1.17. https://doi.org/10.47738/jads.v2i1.17
E. Hutto, C.J. and Gilbert, "VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text," Eighth Int. AAAI Conf. Weblogs Soc. Media, pp. 216-225, 2014. https://doi.org/10.1609/icwsm.v8i1.14550
R. D. Tan et al., "LMS Content Evaluation System with Sentiment Analysis Using Lexicon-Based Approach," 2022 10th Int. Conf. Inf. Educ. Technol. ICIET 2022, pp. 93-98, 2022, doi: 10.1109/ICIET55102.2022.9778976. https://doi.org/10.1109/ICIET55102.2022.9778976
V. Nurcahyawati and Z. Mustaffa, "Vader Lexicon and Support Vector Machine Algorithm to Detect Customer Sentiment Orientation," J. Inf. Syst. Eng. Bus. Intell., vol. 9, no. 1, pp. 108-118, 2023, doi: 10.20473/jisebi.9.1.108-118. https://doi.org/10.20473/jisebi.9.1.108-118
M. Isnan, G. N. Elwirehardja, and B. Pardamean, "Sentiment Analysis for TikTok Review Using VADER Sentiment and SVM Model," Procedia Comput. Sci., vol. 227, pp. 168-175, 2023, doi: 10.1016/j.procs.2023.10.514. https://doi.org/10.1016/j.procs.2023.10.514
F. Illia, M. P. Eugenia, and S. A. Rutba, "Sentiment Analysis on PeduliLindungi Application Using TextBlob and VADER Library," Proc. Int. Conf. Data Sci. Off. Stat., vol. 2021, no. 1, pp. 278-288, Jan. 2022, doi: 10.34123/icdsos.v2021i1.236. https://doi.org/10.34123/icdsos.v2021i1.236
O. Abiola, A. Abayomi-Alli, O. A. Tale, S. Misra, and O. Abayomi-Alli, "Sentiment analysis of COVID-19 tweets from selected hashtags in Nigeria using VADER and Text Blob analyser," J. Electr. Syst. Inf. Technol., vol. 10, no. 1, 2023, doi: 10.1186/s43067-023-00070-9. https://doi.org/10.1186/s43067-023-00070-9
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Elinda Elinda, Herman Yuliansyah, Muhammad Iqbal Abu Latiffi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.