Long Short Term Memory Convolutional Neural Network for Indonesian Sentiment Analysis towards Touristic Destination Reviews

Af'idah, Dwi Intan; Kusumaningrum, Retno; Surarso, Bayu

doi:10.1109/isemantic50169.2020.9234210

Cited by 10 publications

(16 citation statements)

References 17 publications

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“…Kombinasi dari dua metode deep learning atau lebih dapat dilakukan pada suatu analisis sentimen. Penelitian [8] menggunakan dua metode deep learning sekaligus yakni Long Short Term Memory-Convolutional Nerual Network(LSTM-CNN) serta menggunakan Word2Vec sebagai metode pretraining. LSTM-CNN dikombinasikan untuk menjadi solusi dari kelemahan masing-masing yang dimiliki LSTM tunggal dan CNN tunggal.…”

Section: Pendahuluanunclassified

“…Penelitian ini menggunakan dataset berupa ulasan objek wisata Pulau Bali. Hasil sentimen analisis penelitian ini menunjukkan adanya perbaikan akurasi dari metode LSTM-CNN dibandingkan metode LSTM tunggal [8,9].…”

Section: Pendahuluanunclassified

“…Pada penelitian ini data yang digunakan merupakan data yang bersumber dari penelitian [8] berupa sekumpulan teks ulasan objek wisata Pulau Bali berbahasa Indonesia yang diambil dari situs tripadvisor.com. Data tersebut diperoleh menggunakan metode web scraping pada Bulan Februari 2020.…”

Section: Data Penelitianunclassified

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Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Af'idah

Dairoh

Handayani

et al. 2022

matrik

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Pemerintah dan pelaku industri pariwisata mengalami permasalahan dalam menentukan prioritas pengembangan suatu destinasi wisata. Karena itu, diperlukan identifikasi objek wisata yang diminati namun banyak mendapat ulasan buruk melalui ulasan dari masyarakat yang tersebar di internet. Penelitian ini bertujuan melakukan analisis sentimen terhadap ulasan objek wisata di Pulau Bali menggunakan Bi-LSTM dan Word2Vec, sehingga diperoleh model terbaik yang dapat digunakan untuk mengidentifikasi objek wisata potensial namun mendapat ulasan buruk. Bi-LSTM merupakan deep learning yang menawarkan akurasi yang lebih baik daripada LSTM biasa. Sedangkan Word2Vec merupakan pretraining yang dipilih karena dapat menangkap makna semantik teks. Penelitian ini menggunakan data ulasan objek wisata di Pulau Bali yang berasal dari situs tripadvisor.com. Penelitian dimulai dari pengumpulan data, perancangan alur program, preprocessing, pretraining Word2Vec, pembagian data uji dan data latih, pelatihan dan pengujian, serta evaluasi penentuan model terbaik. Akurasi terbaik dihasilkan oleh kombisasi Word2Vec terdiri dari CBOW, Hierarchical Softmax, dimensi 200, Bi-LSTM dengan dropout sebesar 0,5 dan learning rate sebesar 0,0001. Kombinasi tersebut menghasilkan akurasi tertinggi dari keseluruhan 108 kombinasi yaitu sebesar 96,86%, precission sebesar 96,53%, Recall sebesar 96,31%, F1 Measure sebesar 96,41%. Akurasi yang baik tersebut membuktikan bahwa kombinasi parameter Bi-LSTM dan Word2Vec cocok digunakan untuk analisis sentimen ulasan objek wisata di Pulau Bali.

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Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Af'idah

Dairoh

Handayani

et al. 2022

matrik

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“…comprehensive polarity and evaluating a larger amount of material [18]. As a result, we investigate the sentiment analysis model of hotel reviews at the document level.…”

Section: Article Historymentioning

confidence: 99%

Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning

Kusumaningrum

Nisa

Nawangsari

et al. 2021

Int. J. Adv. Intell. Informatics

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Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.

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“…Various studies have been conducted on Indonesian sentiment analysis in various domains [ [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] ]. However, the number of studies on sentiment analysis for Indonesian documents significantly exceeds the number of developed Indonesian sentiment analysis platforms.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

Kusumaningrum

Nisa

Jayanto

et al. 2023

Heliyon

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Long Short Term Memory Convolutional Neural Network for Indonesian Sentiment Analysis towards Touristic Destination Reviews

Cited by 10 publications

References 17 publications

Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Sentimen Ulasan Destinasi Wisata Pulau Bali Menggunakan Bidirectional Long Short Term Memory

Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning

Deep learning-based application for multilevel sentiment analysis of Indonesian hotel reviews

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