Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

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Research Article, J Comput Eng Inf Technol Vol: 7 Issue: 4

Integration Mapping Application: (Web-GIS as Geo- Located Tweets and Sentiment Analysis)

Mohammed Yaqot1* and Abdullatif Albaseer2

1Construction and Engineering Management Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

2Computer Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia

*Corresponding Author : Mohammed Yaqot
Construction and Engineering Management Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
Tel:
+966-3-860-0000
E-mail: [email protected]

Received: August 10, 2018 Accepted: September 29, 2018 Published: October 04, 2018

Citation: Yaqot M, Albaseer A (2018) Integration Mapping Application: (Web-GIS as Geo-Located Tweets and Sentiment Analysis). J Comput Eng Inf Technol 7:4. doi: 10.4172/2324-9307.1000208

Abstract

Nowadays, most people disclose their opinions and decisions toward anything by writing tweets. Analyzing and clustering those opinions known as data mining. Exploiting the integration between the existing technologies, particularly web and geographic information system (GIS), would enable determining people’s location and analyzing their reactions to different aspects of life using the smart engine (sentiment analysis). Marketing sector, for instance, is progressively seeking to generate useful reports that help in analyzing big data by its location to maximize sales and adaptively take corrective actions in the shortest time with least cost. In this paper, a web application has been introduced using nodeJS, JavaScript, MongoDB online database, Twitter APIs, and Google map APIs (including; Geocoding, Reverse Geocoding, and Google map markers). It was found that those tools could integrate, manage, and visualize spatial data with respect to geo-located tweets and its sentiment. Consequently, this application offers convenient, flexible, honest, selective, tailored to different needs, immediate, unrestricted and updated information that help the user to extract features from a large data set of tweets.

Keywords: GIS; MongoDB; Sentiment analysis; Twitter

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