Proposal of Sentiment-based Tourist Spot Recommendation System Using RDF Database

被引：0

作者：

Sakamoto, Yosuke ^{[1
]}

Takama, Yasufumi ^{[1
]}

机构：

[1] Tokyo Metropolitan Univ, Grad Sch Syst Design, 6-6 Asahigaoka, Hino, Tokyo 1910065, Japan

来源：

2017 IEEE 10TH INTERNATIONAL WORKSHOP ON COMPUTATIONAL INTELLIGENCE AND APPLICATIONS (IWCIA) | 2017年

关键词：

Smartphone Application; Recommendation; Sentiment Mining; RDF; Tourist spot recommendation;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a system that recommends tourist spots around the current location relating with the sentiment selected by the user. This system collects tweets and comments about tourist spots from Twitter 1 and Yelp 2, and calculates 10 kinds of emotional scores such as "joy" and "angry" for each spot. Those scores are stored in a RDF (Resource Description Framework) database and used for spot recommendation. Effectiveness of the system is evaluated by applying it to tourist spot recommendation in Nakano (Tokyo, Japan). This paper reports the experimental result in terms of novelty.

引用

页码：61 / 66

页数：6

共 50 条

[31] Recommendation System using Sentiment Analysis considering the Polarity of the Adverb
Guimaraes, Rita
Rodriguez, Demostenes Z.
Rosa, Renata L.
Bressan, Graca
[J]. 2016 IEEE INTERNATIONAL SYMPOSIUM ON CONSUMER ELECTRONICS - 20TH IEEE ISCE, 2016, : 71 - 72
[32] Proposal of the Review Recommendation System using the Concurrent Network
Shiraishi, Ryo
Otake, Kohei
Namatame, Takashi
[J]. PROCEEDINGS OF 2016 FUTURE TECHNOLOGIES CONFERENCE (FTC), 2016, : 616 - 621
[33] A novel stock recommendation system using Guba sentiment analysis
Yunchuan Sun
Mengting Fang
Xinyu Wang
[J]. Personal and Ubiquitous Computing, 2018, 22 : 575 - 587
[34] A novel stock recommendation system using Guba sentiment analysis
Sun, Yunchuan
Fang, Mengting
Wang, Xinyu
[J]. PERSONAL AND UBIQUITOUS COMPUTING, 2018, 22 (03) : 575 - 587
[35] Recommendation System Based on Deep Sentiment Analysis and Matrix Factorization
Liu, Ning
Zhao, Jianhua
[J]. IEEE ACCESS, 2023, 11 : 16994 - 17001
[36] Sentiment-based prediction of alternative cryptocurrency price fluctuations using gradient boosting tree model
Li, Tianyu Ray
Chamrajnagar, Anup S.
Fong, Xander R.
Rizik, Nicholas R.
Fu, Feng
[J]. Frontiers in Physics, 2019, 7 (JULY)
[37] A Survey on Various Aspects of Recommendation System Based on Sentiment Analysis
Mittal, Rohit
Kumar, Sumit
Shrivastava, Vishal
Pathak, Vibhakar
Saini, G. L.
[J]. ARTIFICIAL INTELLIGENCE: THEORY AND APPLICATIONS, VOL 2, AITA 2023, 2024, 844 : 517 - 529
[38] Tourism recommendation system based on semantic clustering and sentiment analysis
Abbasi-Moud, Zahra
Vahdat-Nejad, Hamed
Sadri, Javad
[J]. EXPERT SYSTEMS WITH APPLICATIONS, 2021, 167
[39] Sentiment analysis-based recommendation system for agricultural products
Sharma A.
Vora D.
Shaw K.
Patil S.
[J]. International Journal of Information Technology, 2024, 16 (2) : 761 - 778
[40] Comparative Review on Sentiment analysis-based Recommendation system
Nair, Aditya
Paralkar, Christopher
Pandya, Janya
Chopra, Yash
Krishnan, Deepa
[J]. 2021 6TH INTERNATIONAL CONFERENCE FOR CONVERGENCE IN TECHNOLOGY (I2CT), 2021,

← 1 2 3 4 5 →