# Twitter Data Mining to Map Pedestrian Experience of Open Spaces
###### Abstract
Approximately 200 million people visit the district each year, around 70% of whom arrive by public transport [20]. It is also home to over 38,000 residents and employs over 155,000 people. This location was chosen because the planning phase of the transformation of Oxford Street has just begun, and the implementation phase has not started yet at the moment of writing this work. This gives a good basis to be able to collect data from social networks while having a baseline and comparable research conducted using traditional methods of data collection (i.e., surveys and interviews). It is important to mention that a similar study was done between 1 July 2015 and 29 February 2016 for the predefined places of interest in Belgrade, Serbia, when 2872 tweets were collected [21], but the focus was on tweets with geotags in order to measure the attractiveness of the locations in this city. In accordance with the above-mentioned factors, this research investigates the user-generated data from Twitter to classify and visualize Twitter data sentiment relating to the pedestrian experience of the quality of open spaces, based on their content. It is one of the most popular data sources for research and offers an opportunity to study human communication and social networks [22] because of its open network, allowing access to information published through this platform. Considering the character of social networks and user-generated content, the study reflects tertiary communication as knowledge that is distributed across different individuals and social groups but can be less easily retrieved/usable and operationalised. To find a unique focus for the use of this data, in this research, a sentiment analysis approach was used on Twitter data to map and evaluate user opinions and sentiments related to the specific location, with the goal of classifying them in information clusters about the quality of the pedestrian environment. Classification of Twitter data was done by determining the sentiment of each tweet and classifying them in one of three categories: positive, neutral, and negative sentiments. The results from this analysis were used for consideration of ideas for urban planning of the public space. The location of interest was Oxford Street in London, UK. Accordingly, this work proposes a basis for identifying issues dealing with a certain public space characteristic that can be further analysed in more depth. Therefore, the research question (RQ1) focuses on extracting common topics from Twitter data that are related to certain public space and investigating whether the collective sentiment on this public space can relate to urban design and improvement of pedestrian space. RQ1 aims to extract semantics (topics of interest for pedestrian space) and sentiments (public's opinions about environmental elements relating to public pedestrian space). The following question (RQ2) seeks to validate the finding from RQ1 by comparing it with publicly available data on the community opinions about the public space. The overall goal of this work is to map user experience and opinions about the pedestrian space and to detect sentiment clusters around environmental elements that can be further used for urban and public space design. Given the time- and cost-effectiveness of the proposed model, many of the overlooked opinions and elements can be collected and analysed.