Classification of Tourist Comment Using Word2vec and Random Forest Algorithm
Abstract
Text classification is one of the ways to classify sentences. The grouped data are comments from social media with training data from sites that provide points /scores for each review given such as tripadvisor.co.id. The word2vec method is used to extract words into numbers so that the machine learning algorithm can be applied to classify data. Word2vec is an unsupervised task that is capable of utilizing unlabeled data to convert a word into its vector representation that can also find the semantic relationship between words by counting their distance. The goal from this paper is that data from social media such as Twitter or Instagram can also quickly find out the total /weight of a tourist place from the comment given. The experiment shows that the result of F1 Score on data without removing stop words and eliminate the train data, give a better result 0,85.
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Published
2019-04-15
How to Cite
HABIBI, Isra Nurul; GIRSANG, Abba Suganda.
Classification of Tourist Comment Using Word2vec and Random Forest Algorithm.
Journal of Environmental Management and Tourism, [S.l.], v. 9, n. 8, p. 1725-1732, apr. 2019.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/3117>. Date accessed: 04 dec. 2024.
doi: https://doi.org/10.14505//jemt.v9.8(32).11.
Section
Article
Copyright© 2024 The Author(s). Published by ASERS Publishing 2024. This is an open access article distributed under the terms of CC-BY 4.0 license.