ALS  Vol.2 No.4 , October 2014
A Computational Bible Study of What to Love and What to Hate
Abstract: The Bible comprises the Old Testament of 39 books and the New Testament of 27 books. It can be viewed as the book of love, in which God revealed, out of His unconditional and unchanging love, His plan for the redemption of man in the Old Testament and fulfilled His promise made in the Old Testament by offering the one and only way of salvation through His son Jesus in the New Testament. In this study, we selected the Bible verses that contain the word love or its variation, which were then employed to cluster the books of the Bible with a computational approach. Of the 28 books containing the word love in the Old Testament, seven groups were identified: Genesis, Deuteronomy, Proverbs, Psalms, Song of Songs, First Samuel, and the rest of the other books. From the 26 books containing the word love in the New Testament, five groups were recognized: First Corinthians, John, First John, Luke and Mark and Matthew, and the rest of the other books. Furthermore, the major theme of love in each cluster was also elucidated. The opposite of love is hate. To gain the whole picture of love, we also selected Bible verses that contain the word hate or its variations. From clustering the books containing hate, different contexts of the word hate were recognized, teaching us to hate those that are contrary to love. Taken together, this computational study of the Bible demonstrated that God’s law is designed to love and to love fulfills the law completely and perfectly. Our findings provided a complete catalog of different contexts and themes in which the word love is being presented in the Bible, thereby enabling better understanding of the Bible in this regard.
Keywords: Bible, Love, Clustering
Cite this paper: Hu, W. (2014) A Computational Bible Study of What to Love and What to Hate. Advances in Literary Study, 2, 116-133. doi: 10.4236/als.2014.24019.

[1]   Blei, D., Ng, A., & Jordan, M. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.

[2]   Chapman, G. D. (2009). The 5 Love Languages: The Secret to Love That Lasts. Chicago IL: Northfield Publishing.

[3]   Cowburn, J. S. J. (2003). Love. Milwaukee: Marquette University Press.

[4]   Frey, B. J., & Dueck, D. (2007). Clustering by Passing Messages between Data Points. Science, 315, 972-976.

[5]   Griffiths, T. L., Steyvers, M., & Tenenbaum, J. B. T. (2007). Topics in Semantic Representation. Psychological Review, 114, 211-244.

[6]   Griffiths, T., & Steyvers, M. (2004). Finding Scientific Topics. Proceedings of the National Academy of Sciences, 101, 5228-5235.

[7]   Lamb, R. E. (1997). Love Analyzed. Boulder: Westview Press.

[8]   Pons, P., & Latapy, M. (2006). Computing Communities in Large Networks Using Random Walks. Journal of Graph Algorithms and Applications, 10, 191-218.

[9]   Soble, A. (1989) Eros, Agape, and Philia: Readings in the Philosophy of Love. St. Paul, Minnesota: Paragon House.

[10]   Steyvers, M., & Griffiths, T. (2007). Probabilistic Topic Models. In T. Landauer, D. McNamara, S. Dennis, & W. Kintsch (Eds.), Latent Semantic Analysis: A Road to Meaning. Hillsdale, NJ: Laurence Erlbaum.