TY - GEN
T1 - Analysis of diverse tourist information distributed across the internet
AU - Tsuchiya, Takeshi
AU - Hirose, Hiroo
AU - Miyosawa, Tadashi
AU - Yamada, Tetsuyasu
AU - Sawano, Hiroaki
AU - Koyanagi, Keiichi
N1 - Funding Information:
This work was partly supported by MEXT KAKENHI Grant
Funding Information:
This work was partly supported by MEXT KAKENHI Grant Number 17K01149.
Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - Herein, we propose and discuss a new method for analyzing various types of tourist information about the Suwa area of Nagano Prefecture, Japan, available on the Internet. This information includes not only long sentences that can be found on web pages and in blogs, but also short sentences comprising a few words posted on social media. In this paper, we propose a novel method based on a neural network, called paragraph vector, for expressing relationships between words included in sentences. Our method achieves high retrieval accuracy even across social media posts comprising just a few words. Based on our evaluation results, the proposed method outperforms the conventional information retrieval technique wherein sufficient accuracy cannot be achieved as it is based on the occurrence probability of words in sentences. This improvement is achieved by using the word order as an input feature to the neural network model.
AB - Herein, we propose and discuss a new method for analyzing various types of tourist information about the Suwa area of Nagano Prefecture, Japan, available on the Internet. This information includes not only long sentences that can be found on web pages and in blogs, but also short sentences comprising a few words posted on social media. In this paper, we propose a novel method based on a neural network, called paragraph vector, for expressing relationships between words included in sentences. Our method achieves high retrieval accuracy even across social media posts comprising just a few words. Based on our evaluation results, the proposed method outperforms the conventional information retrieval technique wherein sufficient accuracy cannot be achieved as it is based on the occurrence probability of words in sentences. This improvement is achieved by using the word order as an input feature to the neural network model.
KW - Paragraph vector
KW - SNS
KW - Tourist information
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U2 - 10.1007/978-3-030-03192-3_31
DO - 10.1007/978-3-030-03192-3_31
M3 - Conference contribution
AN - SCOPUS:85082457653
SN - 9783030031916
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 413
EP - 422
BT - Future Data and Security Engineering- 5th International Conference, FDSE 2018, Proceedings
A2 - Dang, Tran Khanh
A2 - Thoai, Nam
A2 - Küng, Josef
A2 - Wagner, Roland
A2 - Takizawa, Makoto
PB - Springer
T2 - 5th International Conference on Future Data and Security Engineering, FDSE 2018
Y2 - 28 November 2018 through 30 November 2018
ER -