Sentiment Analysis Using Recurrent Neural Network (Rnn) Method With Long Short Term Memory (Lstm) On Traveloka Application Comment Review

DOI

10.2991/978-94-6463-520-1_12How to use a DOI?

Keywords

Sentiment; LSTM; Traveloka; Classification; Word Cloud

Abstract

In the industrial era 5.0, everything can be done online. The same applies to travel, whether booking public transportation tickets for a vacation, or booking a hotel room at the desired destination. One example of such an application is Traveloka on the Google Play Store for android users. Long Short Term Memory (LSTM) is one of the popular forms of recurrent neural networks (RNN) specifically designed to solve long-term dependency problems and is particularly suitable for time series processing and prediction. the highest frequency of words in this study is in the word ‘disappointed’ as many as 663, then the frequency of the word ‘easy’ with the number 529, the word ‘buy’ with the number of ferquencies “462”, on the word ‘fast’ with the number “320”, and on the number of words ‘good’ with the number “240” words. Sentiment analysis on Traveloka application comments using the Long Short Term Memory method on the 80:20 training and testing division has an accuracy of 83% correctly.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Husen Yarbo
AU  - Rais Rais
AU  - Iman Setiawan
PY  - 2024
DA  - 2024/12/05
TI  - Sentiment Analysis Using Recurrent Neural Network (Rnn) Method With Long Short Term Memory (Lstm) On Traveloka Application Comment Review
BT  - Proceedings of the 5th International Seminar on Science and Technology (ISST 2023)
PB  - Atlantis Press
SP  - 69
EP  - 77
SN  - 2352-541X
UR  - https://doi.org/10.2991/978-94-6463-520-1_12
DO  - 10.2991/978-94-6463-520-1_12
ID  - Yarbo2024
ER  -