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All Journal Jurnal Pepadun
Ni Putu Ayu Anesca
Jurusan Ilmu Komputer, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Lampung
Computer Science & IT

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SENTIMENT ANALYSIS PROTOKOL KESEHATAN VIRUS CORONA DARI TWEET MENGGUNAKAN WORD2VEC MODEL DAN RECURRENT NEURAL NETWORK LEARNING Ni Putu Ayu Anesca; Kurnia Muludi; Dewi Asiah Shofiana
Jurnal Pepadun Vol. 2 No. 3 (2021): December
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v2i3.86

Abstract

Sentiment analysis is a computational study of opinion from various opinions, which is part of the work that conducts a review related to the computational treatment of opinions, sentiments, and perceptions of the text. To solve various problems in sentiment analysis, needed a good text representation method. In this study, a deep learning analysis was carried out using the Recurrent Neural Network (RNN) method and the Word2Vec Model as word embedding in sentiment classification. The sentiment dataset used comes from user reviews on Twitter (tweets) on the health protocols implemented by the public from the government's appeal. The results showed that the RNN model using sigmoid activation resulted in the greatest accuracy of 66%. The training process in this test uses 10 epochs and 32 batch sizes so that the precision value for negative sentiment is 54% and for positive sentiment is 67%.
Co-Authors Kurnia Muludi Shofiana, Dewi Asiah