Demo of uTool: toollkit to analyze the activity in cities throughout the activity of users in social networks. It includes geolocation, analysis of the interactions and sentiment analysis

1. Introduction UTool geoSA Structural SNA Conclusions
Using geo-tagged sentiment to better understand
social interactions
Elizabeth Vivanco Javier Palanca Elena del Val
Miguel Rebollo Vicent Botti
PAAMS 2017, Porto
@mrebollo
Using geo-tagged sentiment to better understand social interactions

4. Introduction UTool geoSA Structural SNA Conclusions
Introduction
Study of dynamic in cities
Availability of real-time information for decision-making processes
regarding with the uses of the city
citizens as soft-sensors
geolocated resources
activity in social networks publicly available
Identification of sentiments can help to identify problems in the
city
@mrebollo
Using geo-tagged sentiment to better understand social interactions

5. Introduction UTool geoSA Structural SNA Conclusions
Our Purpose
Limitations of current apps
lack of tools to analyze big volumes of geolocated data in
social networks
simplistic sentiment analysis (polarity)
Last development in uTool
basic sentiment analysis of tweets
inclusion of user-defined polygons (geojson)
analysis of explicit interactions among users (conversations)
improvements in the internal architecture to deal with huge
volumes of data
@mrebollo
Using geo-tagged sentiment to better understand social interactions

6. Introduction UTool geoSA Structural SNA Conclusions
Final purpose
Public tool to ease the analysis of the activity in social networks,
including geolocated activity and sentiment analysis, for
non-experts.
creation of retrieval tasks by location or by content
analysis of activity depending on location
identification of hotspots and bursts of activity
study of mobility patters
study of social interactions
analysis of geolocated sentiment information
@mrebollo
Using geo-tagged sentiment to better understand social interactions

7. Introduction UTool geoSA Structural SNA Conclusions
Data
The Open Data portal offers information classified into several
areas
@mrebollo
Using geo-tagged sentiment to better understand social interactions

8. Introduction UTool geoSA Structural SNA Conclusions
Data
The available information can be downloaded in a variety of data
formats, such as csv, shape, geojson, or kml among others
@mrebollo
Using geo-tagged sentiment to better understand social interactions

9. Introduction UTool geoSA Structural SNA Conclusions
U-Tool
U-Tool allows us to monitor the activity of a hashtag or a
geolocated position
@mrebollo
Using geo-tagged sentiment to better understand social interactions

10. Introduction UTool geoSA Structural SNA Conclusions
U-Tool
Individual tweets can be visualized over the map to check their
distribution and density
@mrebollo
Using geo-tagged sentiment to better understand social interactions

11. Introduction UTool geoSA Structural SNA Conclusions
U-Tool
Finally, the gravitational potential is calculated and shown when
tweets include their geographic location.
@mrebollo
Using geo-tagged sentiment to better understand social interactions

12. Introduction UTool geoSA Structural SNA Conclusions
Sentiment Analysis in Twitter
Most of the available tool measure polarity in tweets
@mrebollo
Using geo-tagged sentiment to better understand social interactions

13. Introduction UTool geoSA Structural SNA Conclusions
Geolocated Sentiment Analysis
The activity in social networks can be assigned to a PoI if it falls
under the Voronoi’s region associated to the corresponding PoI
@mrebollo
Using geo-tagged sentiment to better understand social interactions

14. Introduction UTool geoSA Structural SNA Conclusions
Conversational graph
Conversations are extracted from explicit mentions in the messages
and represented in a graph
@mrebollo
Using geo-tagged sentiment to better understand social interactions

15. Introduction UTool geoSA Structural SNA Conclusions
Evolution of network characteristics
The evolution of the main graph measures is calculated
@mrebollo
Using geo-tagged sentiment to better understand social interactions

16. Introduction UTool geoSA Structural SNA Conclusions
User relevance
The relative importance of the users is identified through different
centrality measures
@mrebollo
Using geo-tagged sentiment to better understand social interactions

17. Introduction UTool geoSA Structural SNA Conclusions
Conclusions
analytical tool to study the activity of cities
combines geo-located activity from SS NN and open data
repositories
complex network analysis in real-time
user interaction analysis
geolocated sentiment analysis
@mrebollo
Using geo-tagged sentiment to better understand social interactions

18. Introduction UTool geoSA Structural SNA Conclusions
Next steps
integration of mobility patters
extend the sentiment model
combine conversations and location: does people talk with
nearby persons?
combine conversations and sentiment: does people interacts
with persons that feel the same?
ease the interpretation of the analytics
open the U-Tool dashboard to the public
@mrebollo
Using geo-tagged sentiment to better understand social interactions