@article{MA6A80A48,
title = "Topic Analysis of the National Petition Site and Prediction of Answerable Petitions Based on Deep Learning",
journal = "The Transactions of the Korea Information Processing Society",
year = "2020",
issn = "null",
doi = "https://doi.org/10.3745/KTSDE.2020.9.2.45",
author = "Woo Yun Hui/Hyon Hee Kim",
keywords = "National Petition, Topic Analysis, Topic Modeling, K-means Clustering, LSTM, Deep Learning",
abstract = "Since the opening of the national petition site, it has attracted much attention. In this paper, we perform topic analysis of the national petition site and propose a prediction model for answerable petitions based on deep learning. First, 1,500 petitions are collected, topics are extracted based on the petitions’ contents. Main subjects are defined using K-means clustering algorithm, and detailed subjects are defined using topic modeling of petitions belonging to the main subjects. Also, long short-term memory (LSTM) is used for prediction of answerable petitions. Not only title and contents but also categories, length of text, and ratio of part of speech such as noun, adjective, adverb, verb are also used for the proposed model. Our experimental results show that the type 2 model using other features such as ratio of part of speech, length of text, and categories outperforms the type 1 model without other features."
}