# Twitter Data Mining to Map Pedestrian Experience of Open Spaces
## 4 Discussion
### Twitter Data Mining in Pedestrian Space Quality Analysis  
In this research, special attention was paid to the elements of the pedestrian environment, which were analysed using specific words related to this micro scale such as _bench_, _bin_, _boldar_, _signage system_, _lighting_, _pavement_ (urban equipment and furnishing), _grass_, _trees_, _greenery_ (landscaping elements), _pedestrian_, _car_, _bus_, _taxi_ and _traffic_ (modes of transport). These terms appear in 3638 tweets, which is 15.42% of the total sample. The most common are tweets related to traffic (76.1%), where buses and traffic in general are mentioned as an issue, unlike pedestrians and taxis, which carry positive sentiment (see Figure 5). In relation to urban equipment, the notion of _bins_ is most prevalent in a negative context, while other elements occur to a lesser extent. It should also be noted that the words _trees_ (1.2%), _grass_ (0.5%), and _lighting_ (1.0%), although appearing on a significantly smaller scale, carry positive sentiment. In this way, issues can be clearly separated from desirable elements of pedestrian spaces.  
If these results are compared with the results of public consultations on the issue of respondents' interest in certain topics, a certain analogy can be observed (see Table 6). Thus, the most interest arises in relation to transport (63.6%), landscape (4.6%), amenity (3.5%), lighting (3.4%) and materials (2.1%) [30] (p. 34). Further, specific recommendations from the first public consultations in 2018 [27] referred to pedestrianisation of Oxford Street, levelling the pavement of the road and sidewalks to improve accessibility and extension of the taxi ranks.  
This comparison with the results of targeted research suggests that Twitter can be considered a resource channel in the analysis of pedestrian areas. The assumption is that some other social networks, such as Instagram, which is also popular in Europe [31], will be an even better and richer basis, and that data collected from several social networks can be combined with research. For these purposes, some other methods for sentiment research should be considered to get a better insight of the sentiment of data that are in formats other than text.  
Observing positive and negative sentiment related to individual elements of the pedestrian space can contribute to future decisions of urban designers related to transformation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Results Obtained from Twitter Analysis** & \multicolumn{3}{c}{**Results obtained Using Traditional Tools**} \\ \hline Element & \% & Element & \% \\ Traffic & 76.1\% & Transport & 63.6\% \\ Trees & & & \\ Grass & 1.9\% & Landscape & 4.6\% \\ Greenery & & & \\ Bench & & & \\ Bins & 11.5\% & Amenity & 3.5\% \\ Bollards & & & \\ Lighting & 1.0\% & Lighting & 3.4\% \\ Pavement & 1.6\% & Materials & 2.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the results obtained from Twitter and by using traditional tools in relation to an individual topic.  
proposals. What users have described as positive should be retained and nurtured, while what is characterized as negative should find solutions and improvements. For example, bins are characterized negatively, due to accumulated garbage, irregular maintenance, and high leakage between elements. Accordingly, more regular maintenance and emptying, greater volume of manure and closer distance between individual elements should be proposed. On the other hand, what is characterized positively, such as trees or grass, should be emphasized, the area and number of seedlings should be increased, or some additional alternative solutions in the form of green walls and roofs should be given.