# Image of a City through Big Data Analytics: Colombo from the Lens of Geo-Coded Social Media Data
## 3 Research Design
### Social Media Analytics with the CUP Framework
#### 3.2.2 Understand and Present  
Twitter/Instagram data consist of much information, such as 'created_date', 'username', 'user-screen name', 'text', 'photo/video', and 'user-location'. This study used a descriptive analysis (DA) to provide a larger view of the collected data. This study used three descriptive statistics, namely, Twitter statistics, user analysis, and web-link (URL) analysis. Identifying widely used hashtags is especially useful for urban planners as they reflect the emotive and evaluative reflections toward the places they visit [61]. Twitter statistics provide information about the number of users, number of retweets, and number of hashtags used. This study considered all'retweets' as new tweets.  
Image processing (IP) was conducted to understand the content of Instagram images and their importance to perceive Lynchian elements. With the computational limitationsand the long process of training a large \"Places\" dataset, the study used a publicly available pre-trained model introduced by [PERSON] et al. (2018). [PERSON] et al. (2018) used wide residual networks in his process to train over 10 million image datasets. This model can predict each image with its content, whether it is a \"Skyscraper\", \"Tower\", \"Park\", \"Beach\", etc. It also provides its prediction probability. This probability was used as a weight to measure the final output. The formula used to calculate the probability is given in Equation (1) below.  
\[\text{Probability Sum (class i)}=\sum_{k=1}^{k=n}\text{Probability (class i)} \tag{1}\]  
where \(k\) is the index of the sum, \(n\) is the number of input images and i is the index of the class. Accordingly, the class with the highest probability sum value was selected as the predicted class for the relevant image. Each predicted class was then categorized into the suitable Lychian component based on [PERSON]'s definition of city image components.  
Sentiment analysis (SA) can be identified as the most widely used text classification method to analyze a given textual phase and tell whether the selected phase is positive, negative, or neutral ([PERSON] et al., 2018). This study aims to classify how each tweet describes the respective place depending on the positive and negative factors/words of the tweet. Accordingly, a value ranging between (\(-4\)) and \(+4\) was given to each tweet. To do so, it is required to make a Word Bag of positive and negative words. To make this Word Bag, this study extracted a portion of Positive and Negative words from the already downloaded filtered tweets. Using the obtained data, the positive sentiment values were measured as the ratio of the positive tweets to the total number of tweets, i.e., Positive sentiments = (Positively classified tweets/Total number of tweets) x 100. The study used Weka 2.0 open-source machine learning software and ArcGIS to perform and present the sentiment analysis ([PERSON] et al., 2018; [PERSON] et al., 2018).  
Popularity analysis (PA) was conducted by counting the number of tweets per year posted related to a place name. Popularity increases with the number of tweets tagged. Referring to this, the study analyzed the popularity of each Lychian element among Twitter communities during the past six years. The results of these four analyses are presented in the next section.