Abstract
In recent years, there is a widespread growth of smart cities. These cities
aim to increase the quality of life for its citizens, making living in an urban space
more attractive, livelier, and greener. In order to accomplish these goals, physical
sensors are deployed throughout the city to oversee numerous features such as environmental
parameters, traffic, and the resource consumption. However, this concept
lacks the human dimension within an urban context, not reflecting how humans
perceive their environment and the city’s services. In this context there is a need to
consider sentiment analysis within a smart city as a key element toward coherent
decision making, since it is important not only to assess what people are doing, but
also,why they are behaving in a certainway. In this sense, thiswork aims to assemble
tools and methods that can collect, analyze and share information, based on User
Generated spatialContent and Open Source Geospatial Science. The emotional states
of citizens were sensed through social media data sources (Twitter), by extracting
features (location, user profile information and tweet content by using the Twitter
Streaming API) and applying machine learning techniques, such as natural language
processing (Tweepy 3.0, Python library), text analysis and computational linguistics
(Textblob, Python library). With this approach we are capable to map abstract
concepts like sentiment while linking both quantitative and qualitative analysis in
human geography. Thisworkwould lead to understand and evaluate the “immaterial”
and emotional dimension of the city and its spatial expression, where location-based
social networks, can be established as pivotal geospatial data sources revealing the
pulse of the city.
aim to increase the quality of life for its citizens, making living in an urban space
more attractive, livelier, and greener. In order to accomplish these goals, physical
sensors are deployed throughout the city to oversee numerous features such as environmental
parameters, traffic, and the resource consumption. However, this concept
lacks the human dimension within an urban context, not reflecting how humans
perceive their environment and the city’s services. In this context there is a need to
consider sentiment analysis within a smart city as a key element toward coherent
decision making, since it is important not only to assess what people are doing, but
also,why they are behaving in a certainway. In this sense, thiswork aims to assemble
tools and methods that can collect, analyze and share information, based on User
Generated spatialContent and Open Source Geospatial Science. The emotional states
of citizens were sensed through social media data sources (Twitter), by extracting
features (location, user profile information and tweet content by using the Twitter
Streaming API) and applying machine learning techniques, such as natural language
processing (Tweepy 3.0, Python library), text analysis and computational linguistics
(Textblob, Python library). With this approach we are capable to map abstract
concepts like sentiment while linking both quantitative and qualitative analysis in
human geography. Thisworkwould lead to understand and evaluate the “immaterial”
and emotional dimension of the city and its spatial expression, where location-based
social networks, can be established as pivotal geospatial data sources revealing the
pulse of the city.
| Original language | English |
|---|---|
| Title of host publication | Open Source Geospatial Science for Urban Studies |
| Subtitle of host publication | The Value of Open Geospatial Data |
| Editors | Amin Mobasheri |
| Place of Publication | Gewerbestrasse 11, 6330 Cham, Switzerland |
| Publisher | Springer |
| Chapter | 5 |
| Pages | 75-95 |
| Number of pages | 20 |
| ISBN (Electronic) | 978-3-030-58232-6 |
| ISBN (Print) | 978-3-030-58231-9 |
| DOIs | |
| Publication status | Published - 8 Sep 2020 |
Publication series
| Name | Lecture Notes in Intelligent Transportation and Infrastructure |
|---|---|
| Publisher | Springer |