# Twitter Data Mining to Map Pedestrian Experience of Open Spaces
###### Abstract  
This research investigated the classification and visualisation of Twitter user-generated data. Twitter data were classified based on their sentiment relating to pedestrian experience of the quality of open spaces, based on their content. The research methodology for Twitter data collection, processing and analysis included five phases: data collection, data pre-processing, data classification, data visualisation and data analysis. The territorial focus was on Oxford Street, London, UK. Special attention was placed on the questions regarding the potential of using Twitter data for extracting relevant topics for the public space and investigating whether the sentiment for these topics can relate to urban design and improvement of pedestrian space. The proposed research model considered amount and relevance, its possibilities regarding the interpretation of the collected sample, the potential of the data for the purpose of the analysis of pedestrian space quality, the precision of sentiment determination and the usability of data in relation to a particular open public space. social network data; Twitter; pedestrian experience; open public spaces; Oxford Street +
Footnote †: journal: Medical  
Over the last ten years, digital tools, social networks, and applications have played a main role in our everyday lives [5; 6; 7]. This new way of communicating has already sharpened urban life through more dynamic exchange of information [8; 9]. Furthermore, virtual performances posted by the users of social networks such as Facebook, Instagram, Twitter and other open-generated data may help urban designers to collect necessary information about cognitive and perceptive impressions of the users. Since we are living in a time of smartphone revolution, a new field of research possibilities has opened, due to its technological features. This can be seen in the growing interest across urban design in understanding the role played by new location-based media and the impact of the increasing availability of urban digital data from various sources [10]. With the development of social media and social networks, online platforms [11] have become a place for sharing opinions and not just a place for sharing details about personal daily lives. Social network users use these platforms to express their opinions about services, products, events, public individuals, public locations, etc. The experience of citizens and visitors in cities can now be shared through pictures, videos, posts, and check-ins, while social media has become a vital information source through which different urban phenomena can be explored [12; 13] and can help to explain a variety of spatial-temporal phenomena [14]. One aspect of the shared urban environment which is particularly important for planners and policy makers is pedestrian space. Analysis of social media data can give insights into the quality of this space, which can then be used to improve or support its use. For several years now, much attention has been paid to the transformation of the main city streets of major European cities. Many streets are in capitals such as Paris, Madrid, Brussels, Oslo, Vienna, Copenhagen, Ljubljana, London, etc., and these world-famous streets attract many users, visitors and tourists. On the other hand, the main city street is much more than a place; it is also a state of mind and a set of values [15]. Transformation of the main city street can have a significant effect in relation to future urban practices and values but also in relation to the quality of life of the citizens of that city. Having in mind several initiatives and plans concerning the transformation of Oxford Street in London [16; 17] and the very nature of the changes envisaged for it [18; 19], this street was taken as an appropriate example to investigate the potential of user-generated data related to public space. In addition to the above, the Oxford Street District is a major part of London's West End and among the world's most visited destinations.