Bible analyzer alternate font6/1/2023 ![]() The code used to create the vectorizer is below. Specifically, we used the sklearn package. This method of vectorization extracts the frequency of each term in a text and adds the inverse of each term frequency among all documents. The vectorization method used here was the Term Frequency - Inverse Document Frequency (TF-IDF). ![]() df.text = for t in stopwords.words( 'english'): df.text = Vectorization - Transforming text in numbers df = 0 firstMat = df.loc = 'Matthew', :].index df.loc = 1įinally, we replaced the new line char (‘\n’) and removed the stopwords. As the rows had the same order as in the bible, we just chose the first book of the New Testament and labeled all the other rows below. We also created a feature to identify the Testament as old or new. df.book = df = for r in df.book] df = if len(r.split( '_')) = 2 else r.split( '_') '_' r.split( '_') for r in df.book] For the analysis of books, the chapters were concatenated. The code and the main issues in that work will be published in a new story in the future. Regarding frameworks, Selenium was used on Python. I really enjoy reading this version, so I chose it to use in the analysis. Web Scraping and Preprocessing - NET Bibleĭata was Scraped from the NET Bible portal, a new free translation made by a group of scholars that comes with many notes made by them. Apart from religions, the intent here is to try to understand better the context of when the Bible was written and if the series of divisions (Chapters, New / Old Testament, Gospels) makes sense in terms of NLP. NLP of Bible Chapters and Books - Similarity and Clustering with Python
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