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Journal : Bulletin of Electrical Engineering and Informatics

Enhance sentiment analysis in big data tourism using hybrid lexicon and active learning support vector machine Saraswati, Ni Wayan Sumartini; Ketut Gede Darma Putra, I; Sudarma, Made; Made Sukarsa, I
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7807

Abstract

Sentiment analysis is a review analysis process used to determine whether an opinion is neutral, negative, or positive. Sentiment analysis can be done using lexicon-based or machine learning-based approaches. Lexicon can perform sentiment analysis without training data because it is dictionary-based but performs worse than machine learning. Machine learning can perform well in completing sentiment analysis but requires training data so that the model does not experience underfitting. In the case of sentiment analysis on big data, manual labeling of training data is an inefficient job. Support vector machine (SVM) has the opportunity to be used together with the active learning (AL) method to make small training data but still have good performance. This research proposed a hybrid lexicon and AL-SVM method to complete sentiment analysis on big data tourism. This research used polarity from the valence aware dictionary and sentiment reasoner (VADER) lexicon as a reference for the query by user process from the AL-SVM to automate the sentiment analysis process on big data. The experimental results showed that using the hybrid lexicon and AL-SVM increased the sentiment analysis performance compared to the VADER lexicon, SVM, and lexicon SVM, which run separately.
Recency, frequency, quality: novel feature from sentiment analysis for clustering and ranking in tourism big data analytics Saraswati, Ni Wayan Sumartini; Putra, I Ketut Gede Darma; Sudarma, Made; Sukarsa, I Made; Aristamy, I Gusti Ayu Agung Mas
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.10709

Abstract

Understanding tourist perceptions has been a key benefit of sentiment analysis in tourism data. However, its outcomes can be further utilized to gain insights into the characteristics of tourist attractions and hotels. This study aims to develop a new feature, called recency, frequency, quality (RFQ), derived from sentiment analysis results to cluster and rank tourist attractions and hotels in Bali. RFQ consists of three components: review recency, review frequency, and review quality. These dimensions reflect the recentness of reviews, the popularity based on the number of reviews, and the review quality measured by the ratio of positive to negative sentiment polarity. Using big data analytics through clustering and ranking, the study finds that the quality of tourist attractions and hotels is primarily concentrated in Badung and Gianyar regencies. More tourist attractions are found in the silver cluster than in the gold, indicating the need to enhance quality. In the hotel sector, the diamond cluster dominates among star-rated hotels, suggesting overall high quality. Budget hotels show fairly good quality, with most falling under the gold cluster.
Co-Authors Alvin Limawan Susanto Andika, I Gede Aristamy, I Gusti Ayu Agung Mas Atmaja, Ketut Jaya Baehaqi Christina Purnama Yanti Christina Purnama Yanti Dewa Ayu Putu Rasmika Dewi Dewa Ayu Putu Rasmika Dewi Dewa Ayu Putu Rasmika Dewi Dewi Natalia, Sang Ayu Made Krisna Dewi, Dewa Ayu Putu Rasmika Dewi, Yesi Ratna Eddy Hartono Eddy Hartono Eddy Hartono Eddy Hartono I Dewa Made Krishna Muku I Dewa Made Krishna Muku I Dewa Made Krishna Muku I Gede Adi Sudi Anggara I Gusti Ayu Agung Diatri Indradewi I Kadek Agus Bisena I Kadek Agus Bisena I Kadek Putra Agung Darmawan I Ketut Gede Darma Putra I Ketut Setiawan I Made Andi Kertha Yasa I Made Sukarsa I Nyoman Tri Anindia Putra I Nyoman Yudha Chandra Dinata I Putu Dedy Sandana I Putu Dedy Sandana I Putu Krisna Suarendra Putra I Wayan Agustya Saputra I Wayan Dharma Suryawan Ida Bagus Gede Sarasvananda Juniartini, Ni Komang Tri Kadek Budi Sandika Ketut Gede Darma Putra, I Ketut Laksmi Maswari Ketut Sepdyana Kartini Krismentari, Ni Kadek Bumi Krisna, Gede Gana Eka Made Sudarma MADE WAHYU ADHIPUTRA Maria Osmunda Eawea Monny Melinia Hutari Natalia, Sang Ayu Made Krisna Dewi Ni Komang Tri Juniartini Ni Luh Pangestu Widya Sari NI LUH PUTU AGETANIA . NI LUH PUTU MERY MARLINDA Ni Made Lisma Martarini Ni Wayan Mirah Senja Pertiwi Ni Wayan Wardani Nirwana, Ni Kade Ayu Pirozmand, Poria Poria Pirozmand Poria Pirozmand Poria Pirozmand Poria Pirozmand Pramana, I Gusti Kadek Candra Adi Cahya Pramest, Ni Luh Gede Sintia Pramita, Dewa Ayu Kadek Pramitha, Gede Dana Putu Ananda Sitarasmi Putu Wirayudi Aditama Sandhiyasa, I Made Subrata Sari, Ni Luh Pangestu Widya Waas, Devi Valentino Wardani, Ni Wayan Weizhi Song