Home| Contact Us| New Journals| Browse Journals| Journal Prices| For Authors|

Print ISSN: 0976-416X
Online ISSN:
0976-4178


  About IJCLR
  DLINE Portal Home
Home
Aims & Scope
Editorial Board
Current Issue
Next Issue
Previous Issue
Sample Issue
Upcoming Conferences
Self-archiving policy
Alert Services
Be a Reviewer
Publisher
Paper Submission
Subscription
Contact us
 
  How To Order
  Order Online
Price Information
Request for Complimentary
Print Copy
 
  For Authors
  Guidelines for Contributors
Online Submission
Call for Papers
Author Rights
 
 
RELATED JOURNALS
Journal of Digital Information Management (JDIM)
Journal of Multimedia Processing and Technologies (JMPT)
International Journal of Web Application (IJWA)

 

 
International Journal of Computational Linguistics Research
 

 

Analyses of Geo-Referenced Twitter Data for Understanding Spatial Distribution and Content Classification
Nikola Dzakovic, Nikola Dinkic, Jugoslav Jokovic, Leonid Stoimenov, Dejan Rancic
University of Niš, Faculty of Electronic, Engineering, Aleksandra Medvedeva 14 & 18000 Niš, Serbia
Abstract: We intend to study the spatial patterns of the urban regions and design it for open spaces. To do so we have employed the user-generated twitter data that could support and improve the understanding. The features such as temporal and spatial distribution, content classification, language determination and sentiment analyses are studied using the data generated by Twitter social network. We have sued the Twitter search engine application to classify and process the geo-referenced tweets collected.
Keywords: Twitter Data, Geo Reference Data, Text Analysis, Text Mining Analyses of Geo-Referenced Twitter Data for Understanding Spatial Distribution and Content Classification
DOI:https://doi.org/10.6025/jcl/2021/12/2/35-42
Full_Text   PDF 3.27 MB   Download:   599  times
References:

[1] Dzakovic, N., Dinkic, N., Jokovic, J., Stoimenov, L. (2016). Web Application for Mining, Storing, Processing and Geoanalysis Data from Twitter Social Network, YU INFO, Kopaonik, Serbia, (March).
[2] https://dev.Twitter.com/rest/public, accessed on March 15th, 2017.
[3] Huang, L., Li, Q., Yue, Y. (2010). Activity Identification from GPS Trajectories using Spatial Temporal POIs’ Attractiveness, *ACM SIGSPATIAL Workshop on Location Based Social Networks, San Jose, USA, p. 27-30.
[4] Vukmirovic, M., Vanista Lazarevic, E. (2015). Competitiveness Express though Digital Data, in E. Vanista Lazarevic, M. Vukmirovic A. Krstic-Funurndzic, and A. Djukic (Eds.), Keeping up with Technologies to Improve Places, Newcastle upon Tyne: Cambridge cholras Publishing.
[5] Furtado, A. S., Fileto, R., Renso, C. (2013). Assessing the Attractiveness of Places with Movement Data, Journal of Information and Data Management, 4 (2) 124-133, (June).
[6] Breser, C., Zedlacher, S., Winkler, R. a. (2016). The Principle of Geotagging. Cross-linking Archival Sources with People and the City Through Digital Urban Places, ICiTy Conference, Valletta, Malta, 18-19 (April).
[7] Hu, M., Liu, B. (2004). Mining Opinion Features in Customer Reviews, American Association for Artificial Intelligence.
[8] Liu, B. (2012). Sentiment Analysis an d Opinion Mining.
[9] Pandey, V., Krishnakumar Iyer, C. V. (2009). Sentiment Analysis of Microblog, CS 229: Machine Learning Final Projects, 2009 - cs229.stanford.edu
[10] Language Detection API -https://detectlanguage.com/, last visited 15.03.2017.


Home | Aim & Scope | Editorial Board | Author Guidelines | Publisher | Subscription | Previous Issue | Contact Us |Upcoming Conferences|Sample Issues|Library Recommendation Form|

 

Copyright © 2011 dline.info