# Getting over High-Dimensionality: How Multidimensional Projection Methods Can Assist Data Science
## 5 Discussions about the Multidimensional Projection Techniques
### Real Data Applications
#### 5.1.1 Text Mining Data  
The first motivation was adopted from the KAGGLE website, which is related to the Women's Clothing E-Commerce database. These data were anonymous recorded reviews derived from customers, which its dataset shows ten features (Clothing ID, reviewers Age, Title, Review Text, Rating, Recommended IND, Positive Feedback Count, Division Name, Department Name, and Class Name). Further details can be found at [107].  
After pre-processing steps such as removing numbers, punctuation, and stopwords and transforming them all into lowercase, the frequency of the terms was calculated.  
Figure 4: Comparative charts presenting stress and running times between some multidimensional projection techniques. Source: [PERSON] et al. [6].  
Figure 5 shows the description of the Review Text feature, a suitable analysis that helps verify words' importance and their relation with the recommendations.  
In addition to these analyses, the multidimensional projection algorithms can cluster observations alike embedded in 2D. Figure 6 shows the MP exemplification considering the review from the item Jacket only, adopting the algorithms PCA, MDS, Force Scheme, LAMP, t-SNE, and UMAP, whereas the blue words are used in positive comments, and the red used in negative comments. Whereas these engines are helpful in monitoring and tracing some characteristics that can maximize the rating from the clothing store. In this case, it is challenging to identify clusters between positive and negative comments. Thus, other types of features could be considered to represent that segregation. On the other hand, one can analyze isolated clothing classes to determine the segregation between positive and negative comments.  
Figure 5: Text summary: the 20 words more frequent (left), and a word cloud of 100 terms colored by the recommended (positive or negative).  
Figure 6: Projections of the user reviews, colored by the recommended labels (negative or positive). Source: elaborated by the authors.  
For instance, by selecting PCA visualization and adopting the package _factoextra_, the text reviews can be seen through their similarity (in Figure 7). Selecting the extreme values on the top right (ID #8551) versus the bottom right (ID #12374), it is easy to detect we have a positive review versus a negative one. Moreover, those obtained patterns can unravel other characteristics across the formed clusters.  
Review ID #8551--\"This brand makes the best jackets. I got a vest from them last year and an olive moto jacket. Both fit amazing. This one is no different. It fits great! I love that it is a great casual jacket with structure and drape. I cannot say enough good things about this Jacket. Every time I wear it, I feel so pulled together. Fun light gray color that you do not see too often in jackets. This is a jacket I will see in my wardrobe for years to come. must-have\".  
Review ID #12374--\"This is a very loose-fitting style jacket. If you like your jackets oversized, get your regular size; if you prefer the slightly loose (but not baggy) fit like the product photo, size down. As noted by another reviewer, the sleeves are incredibly long. I would say if you are a typical size 0 or 2 (unless you like the oversized fit), you are likely sized out of this Jacket. It is a shame because the quality is great, and the windowpane print is beautiful. I am sadly sized out\".