TITLE: Text Mining Approach for Adapting a School-Based Sexual Health Promotion Program in Colombia
AUTHORS: Pablo Vallejo-Medina, Juan C. Correa, Mayra Gómez-Lugo, Diego Alejandro Saavedra, Eileen García-Montaño, Diana Pérez-Pedraza, Janivys Niebles-Charris, Paola García-Roncallo, Daniella Abello-Luque, José Pedro Espada, Alexandra Morales

[DESCRIPTION]
<div class = "text-blocks"><div class = "text-block">A common practice among clinical psychologists and other health professionals is the use of school-based sexual health promotion programs as a means for preventing sexually transmitted infections. A fundamental criterion for the designing and adaptation of these programs is the age of their target populations because limited education and language are the most relevant factors that limit the efficacy of these programs. In this paper, we proposed a methodological approach that facilitates the empirical evaluation of the written materials that accompany the school-based sexual health promotion programs, taken as a case a Spanish-written program used in Colombia. The results showed the empirical adequacy of this program while positing novel insights for scrutinizing the efficacy of similar programs.  </div></div>

[STEPS]
?. Download the following five txt-formatted files. and put these files inside a folder with the name "COMPAS".
?. If you are using a Linux distro, you can put that folder in the following path: "/home/juan/Documents/COMPAS". In Windows, you can put it in the following path: "C:\Users\juan\Documents\COMPAS\"
?. Download the following R-script and run it accordingly. Note, that in line 10 of this script you will see this code:folder Make sure to replace the path where you downloaded the folder COMPAS and edit the above code accordingly.
?. In line 11 you will see this code:sesiones After running that code, check that the following output is truesesiones[1] "Sesion1.txt" "Sesion2.txt" "Sesion3.txt" "Sesion4.txt" "Sesion5.txt"Otherwise, run line 11 again
?. Run the following codelibrary(koRpus)ID results tok Escolaridad Edad library(koRpus.lang.es)for(i in seq_along(sesiones)){  results[[i]]   tok[[i]]   Escolaridad[[i]]   Edad[[i]] }DifText hist(DifText$Escolaridad, xlab = "Años de Escolaridad", col = "lightblue")library(ggplot2)ggplot(DifText, aes(DifText$ID, DifText$Escolaridad)) + geom_bar(stat = "identity", fill="red") + xlab("Session Number") + ylab("Years of Education")df2                   Session=rep(c("Session1", "Session2", "Session3","Session4","Session5"),2),                  Years=c(13.12, 12.93, 12.67, 11.62, 14.46, 18.12, 17.93,17.67, 16.62, 19.46))ggplot(data=df2, aes(x=Session, y=Years, fill=Variable)) +  geom_bar(stat="identity", position=position_dodge()) +  geom_text(aes(label=Years), vjust=1.6, color="white",position = position_dodge(0.9), size=3.5)
?. A second part of the analyses entails the visualization of the network of words co-occurrence In KH Coder. For this process,  we removed Spanish stopwords (e.g., articles, prepositions, etc.) as well as frequent verbs such as: “can,” “to be,” “to have,” and non-informative words such as “case,” “question” and “answer.”. Secondly, we removed regular expressions (i.e., hola, bienvenido, etc.) and ordinal adjectives (firstly, second, etc.) from the corpus analysis (i.e., a total of 8 new words irrelevant for the program). We set the sentences as the unit of analysis and employed a minimum term frequency of 10 as a threshold to depict words co-occurrence, along with a Jaccard similarity index as the filter for connexions between words.  We colored our resulting networks following a betweenness centrality criterion, which stands for a metric that quantifies the statistical importance of a word, represented as a node within a network that shows the connexion between words. In addition, we used the following coding rules for merging semantic similar words.