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Fuzzy Domain Ontology-based Opinion Mining for Transportation Network Monitoring and City Features Map
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 Title & Authors
Fuzzy Domain Ontology-based Opinion Mining for Transportation Network Monitoring and City Features Map
Ali, Farman; Kwak, Daehan; Islam, SM Riazul; Kim, Kye Hyun; Kwak, Kyung Sup;
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Traffic congestions are rapidly increasing in urban areas. In order to reduce these problems, it needs real-time data and intelligent techniques to quickly identify traffic activities with useful information. This paper proposes a Fuzzy Domain Ontology(FDO)-based opinion mining system to monitor the transportation network in real-time as well to make a city polarity map for travelers. The proposed system retrieves tweets and reviews related to transportation activities and a city. The feature opinions are extracted from these tweets and reviews and then used FDO to identify transportation and city features polarity. This FDO and intelligent prototype are developed using OWL (Web Ontology Language) and JAVA, respectively. The experimental result shows satisfactory improvement in tweets and review`s analyzing and opinion mining.
Opinion mining;Fuzzy domain ontology;Social network;Transportation;Polarity map;
 Cited by
Liu B., Hu M. and Cheng J.(2005), "Opinion observer: Analyzing and comparing opinions on the Web," in Proc. 14th Int. Conf. World Wide Web, pp.342-351.

Cao J., Zeng K. and Wang H.(2014), "Web-based traffic sentiment analysis: Methods and Applications," IEEE transactions on Intelligent Transportation systems, vol. 15, pp.844-853. crossref(new window)

Kim S. M. and Hovy E.(2006), "Extracting opinions, opinion holders, and topics expressed in online news media text," in Proc. Workshop Sentiment Subj. Text, pp.1-8.

Ali F., Kim E. K. and Kim Y. G.(2015), "Type-2 fuzzy ontology-based opinion mining and information extraction: A proposal to automate the hotel reservation system," Applied Intelligence, vol. 42, pp.481-500. crossref(new window)

Jeong H., Shin D. and Choi J.(2011), "FEROM: Feature Extraction and Refinement for opinion Mining," ETRI, vol. 33, pp.720-730. crossref(new window)

Kawathekar S. A. and Kshirsagar M. M.(2012), "Movie review analysis using Rule-based and support vector machines methods," Journal of engineering, vol. 2, pp.389-391.

Syeedunnissa S. F., Hussain A. R. and Hameed M. A.(2013), "Supervised opinion mining of social network data using a Bag-of-Words approach on the cloud," Advance in Intelligent Systems and Computing, vol. 2, pp.299-309.

Gilboa S., Jaffe E. D., Vianelli D., Pastore A. and Herstein A.(2015), "A summated rating scale for measuring city image," Cities vol. 44, pp.50-59. crossref(new window)

Bertrand K. Z., Bialik M., Virdee K., Gros A. and Yam Y. B.(2013), "Sentiment in New York City: a high resolution spatial and temporal view," New England Complex Systems Institute.

Bobillo F. and Straccia U.(2011), "Fuzzy ontology representation usnig OWL 2," Approx Reason, vol. 52, pp.1073-1094. crossref(new window)

Dongli Y., Suihua W. and Ailing Z.(2009), "Traffic accidents knowledge management based on ontology," International conference on fuzzy systems and knowledge discovery, pp.447-449.

Lin C. J. and Chao P. H.(2010), "tourism-Related Opinion Mining," 22nd conference on computational linguistics and speech processing, pp.3-16.

Ali F., Kim E. K. amd Kim Y. G.(2015), "Type-2 fuzzy ontology-based semantic knowledge for collision avoidance of autonomous underwater vehicles," Information Sciences, vol. 295, pp.441-464. crossref(new window)

Zadeh L. A.(1965) "Fuzzy sets," Inf Cont, vol. 8, pp.338-353. crossref(new window)

Baccianella B., Andrea E. and Fabrizio S.(2010), "SENTIWORDNET 3.0: An Enhanced Lexical Resource for Sentiment Analysis and Opinion Mining," Conference on International Language Resource and Evaluation.