# Twitter Data Mining to Map Pedestrian Experience of Open Spaces
## 3 Results  
By processing the collected data, using the previously mentioned methodology, the results were obtained, which are presented below. Figure 1(a),c represent the words that appear with the highest frequency in tweets, with positive, negative, and neutral sentiment, respectively. Besides the keywords used for data collection, such as \"oxford street\", most frequently used words in positive tweets are _love_, _today_, _look_, _good_, _work_, _life_, _smile_ and _feel_. Similarly, \begin{table}
\begin{tabular}{l l} \hline \hline
**Tweets BEFORE the Pre-Processing** & **Tweets AFTER the Pre-Processing** \\ \hline Think will be hitting Regent Street more often \(\ldots\) Happy to say & think will be **hit** Regent Street more often happy to say \\ people have started coming back out, Oxford Circus, shops, & people have started coming back out oxford circus **shop**, \\ restaurants, and theatres were buzzing and busy! Loving it! & **restaurant** and **theatre** were buzzz and busy **love** it \\ Street photography in Oxford. Waiting for the bus can be boring & street photography in oxford **wait** for the bus can be \\ especially when you are tired from walking around a city as a & boring especially when you are tire from walk around a \\ tourist. This is part of a series of candid shots of people waiting & city as a tourist \\ at a bus stop. & this is part of a series of candid **shot** of people wait at a \\  & & bus stop \\ \#streeptphotography \#oxford \#bustop \#waiting \(\ldots\) & good news they are **add** turn **lane** at the laundromat \\ https://tco/N30 bbHJED https://tco/2 gYy8 Uoa5 & \(\rightarrow\) & good news they are **add** turn **lane** at the laundromat \\ Good news, they are adding turn lanes at the laundromat in & in oxford \\ Oxford. Bad news, the stadium across the street is holding & bad news the stadium across the street is **hold** graduation \\ graduation and people ignored the no parking signs. & and people **ignore** the no parking **sign** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Example of tweets before and after the pre-processing. Figure 2: Word cloud visualisation of (a) tweets with positive, (b) negative and (c) neutral sentiment. for negative tweets the most frequently used words are _London_, _people_, _attack_, _ Jewish_, _protest_, _stop_, _fire_, _hate_; while in neutral tweets the most frequently used words are _London_, _shop_, _flagship_, _Christmas_, _park_, _place_ and _Selfridge_ (Table 2). As data was collected based on the assigned keywords and hashtags, it was of interest to analyse what were other related hashtags showing up in the collected tweets. Table 3 provides this information based on the tweet sentiment, giving the number of tweets in which related hashtags appear and the percentage of the total number of positive, negative and neutral tweets (Figure 3). Considering the overall sentiment of the collected tweets, the analysis shows that 40.40% of tweets carry positive sentiment, while 27.73% are negative and 31.87% are neutral (Table 4). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Word**} & **Count in** & **Count in** & **Count in** \\  & **Positive Tweets** & **Negative Tweets** & **Neutral Tweets** \\ \hline street & 7604 & 6250 & 8870 \\ oxford & 7058 & 5677 & 8042 \\ love & 1541 & 0 & 0 \\ today & 1221 & 0 & 0 \\ look & 842 & 0 & 0 \\ good & 801 & 0 & 0 \\ work & 772 & 0 & 0 \\ life & 766 & 0 & 0 \\ smile & 659 & 0 & 0 \\ feel & 651 & 0 & 0 \\ London & 0 & 1000 & 1550 \\ shop & 0 & 0 & 1001 \\ walk & 0 & 0 & 544 \\ flagship & 0 & 0 & 393 \\ Christmas & 0 & 0 & 382 \\ park & 0 & 0 & 374 \\ place & 0 & 0 & 303 \\ Selfridge & 0 & 0 & 288 \\ people & 0 & 579 & 0 \\ attack & 0 & 471 & 0 \\ Jewish & 0 & 461 & 0 \\ protest & 0 & 350 & 0 \\ stop & 0 & 291 & 0 \\ fire & 0 & 253 & 0 \\ hate & 0 & 160 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Most frequently used words in tweets with positive, negative, or neutral sentiment.