Social networks VGI: Twitter sentiment analysis of social hotspots
Journal
European Handbook of crowdsourced geographic information
Date Issued
2016-08-25
Author(s)
Stojanovski, Dario
Abstract
The enormous amount of data generated on social media provides vast quantities of geo-referenced data. Volunteered Geographic Information (VGI) originating from social networks has produced new challenges for research and has
opened opportunities for a wide range of use cases. Smartphones with built-in
GPS sensors enabled users to easily share their location and with the growing
number of such devices available, VGI data is expanding at a rapid rate. Twitter is one of the most popular microblogging services. It’s a social network that
enables access to the data that is being created on the platform. It also allows for
real-time retrieval of data from a given geographic area.
In this paper we give an overview of a system for detecting and identifying
social hotspots from Twitter stream data and applying sentiment analysis on
the data. Utilizing the Twitter Streaming Application Programming Interface
(API), we collected a significant number of Tweets from New York and we
evaluated the quality of the retrieved data. In this paper, we outline advantages
and disadvantages of using various clustering algorithms over the data for this
purpose, namely hierarchical agglomerative clustering and DBSCAN. We also
elaborate on techniques for identifying social hotspots from spatially localized clusters. Finally, we present a deep learning approach to sentiment analysis
used to determine the attitude of users participating in the identified social
hotspots.
opened opportunities for a wide range of use cases. Smartphones with built-in
GPS sensors enabled users to easily share their location and with the growing
number of such devices available, VGI data is expanding at a rapid rate. Twitter is one of the most popular microblogging services. It’s a social network that
enables access to the data that is being created on the platform. It also allows for
real-time retrieval of data from a given geographic area.
In this paper we give an overview of a system for detecting and identifying
social hotspots from Twitter stream data and applying sentiment analysis on
the data. Utilizing the Twitter Streaming Application Programming Interface
(API), we collected a significant number of Tweets from New York and we
evaluated the quality of the retrieved data. In this paper, we outline advantages
and disadvantages of using various clustering algorithms over the data for this
purpose, namely hierarchical agglomerative clustering and DBSCAN. We also
elaborate on techniques for identifying social hotspots from spatially localized clusters. Finally, we present a deep learning approach to sentiment analysis
used to determine the attitude of users participating in the identified social
hotspots.
Subjects
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