6.6 The moment of truth

Now comes the final stage where we check the correlations between various NSS measures and ACSI. For this I use ggcorplot() function from ggcorplot package. As this is not a major topic for this exercise, I leave the explanation of the code to you as an exercise.

ggcorrplot::ggcorrplot(
  airlines_final %>% 
    select(starts_with("nss"), acsi) %>% 
    cor() %>% 
    round(2), 
  p.mat = ggcorrplot::cor_pmat(
    airlines_final %>% 
      select(starts_with("nss"), acsi)
    ),
  hc.order = TRUE, 
  type = "lower",
  outline.color = "white",
  ggtheme = ggplot2::theme_minimal,
  colors = c("#cf222c", "white", "#3a2d7f")
  )
Correlation Plot

Figure 6.1: Correlation Plot

From Figure 6.1 it looks like ACSI has somewhat negative correlations with each of the NSS metric! This is not good news…for ACSI! :)Furthermore, the crosses on the squares indicate statistical non-significance. However, as I explain below, we will do a better comparison with more direct sentiment metrics.

Table 6.3 shows the correlations in numbers. Indeed, ACSI is marginally negatively correlated with NSS metrics.

Table 6.3: Sentiment and ACSI Correlations
nss nss_fav nss_rt acsi
nss 1.000 0.528 0.518 0.138
nss_fav 0.528 1.000 0.857 -0.297
nss_rt 0.518 0.857 1.000 -0.251
acsi 0.138 -0.297 -0.251 1.000