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The careful examination of sacred texts gives valuable insights into humanpsychology, different ideas regarding the organization of societies as well asinto terms like truth and God. To improve and deepen our understanding ofsacred texts, their comparison, and their separation is crucial. For thispurpose, we use our data set has nine sacred scriptures. This work deals withthe separation of the Quran, the Asian scriptures Tao-Te-Ching, the Buddhism,the Yogasutras, and the Upanishads as well as the four books from the Bible,namely the Book of Proverbs, the Book of Ecclesiastes, the Book ofEcclesiasticus, and the Book of Wisdom. These scriptures are analyzed based onthe natural language processing NLP creating the mathematical representation ofthe corpus in terms of frequencies called document term matrix (DTM). Afterthis analysis, machine learning methods like supervised and unsupervisedlearning are applied to perform classification. Here we use the MultinomialNaive Bayes (MNB), the Super Vector Machine (SVM), the Random Forest (RF), andthe K-nearest Neighbors (KNN). We obtain that among these methods MNB is ableto predict the class of a sacred text with an accuracy of about 85.84 %.

A Text Mining Discovery of Similarities and Dissimilarities Among Sacred Scriptures