Exploring the Bible through NLP: A new approach to understanding scripture
Topic Modeling, Contextual Analysis, Text Classification
Topic modeling is a powerful tool for analyzing and organizing large collections of text data. It allows researchers to uncover the underlying themes and topics present in a text, and to explore how these themes and topics evolve over time. In this study, will apply topic modeling to the Bible, a text that has been central to Western culture for centuries and continues to be widely studied and analyzed by scholars from a variety of disciplines.
Religious text research is frequently conducted by literary and philosophy scholars, as well as those educated in divinity schools. The study reveals deeper insights into religious passages than mere words or phrases. This focus on the meaning of biblical passages yields insights into religious beliefs and practices. Such study typically employs qualitative and critical/rhetorical methods. Such methodologies are applicable because passages may have multiple levels of meaning and symbolism and do not fit into the frequently simplified categories of quantitative study.
The Bible is a complex and multifaceted text, encompassing a wide range of genres, themes, and perspectives. It consists of 66 books, written by multiple authors over the course of many centuries, and includes both historical and religious narratives, as well as poems, letters, and prophecies. As such, it presents a unique challenge for topic modeling, as it is not a single, unified text but rather a collection of texts with diverse content and structures.
Despite these challenges, the Bible has been the subject of numerous studies using topic modeling, and these studies have revealed a number of interesting insights about the text and its themes. In this study, it aims to build upon this existing work by applying state-of-the-art topic modeling techniques to the Bible, with the goal of uncovering new insights and patterns in the text.
To accomplish this goal, the first step is to preprocess the Bible text to prepare it for analysis. This will involve tokenizing the text, removing stop words, and stemming the words to their root form. Then applying topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), to the preprocessed text in order to identify the underlying topics present in the Bible. Finally, the analysis and interpretation of the resulting topic models.
Overall, this study aims to contribute to the understanding of the Bible as a complex and multifaceted text, and to provide new insights into its themes and structures. By applying topic modeling techniques to the Bible, it will shed light on the underlying patterns and trends present in the text, and to provide a new perspective on this important cultural and historical document.
Data Set Description
The dataset for this study consists of the complete text of the Bible, including both the Old Testament and the New Testament. The text is provided in the English Standard Version, which is a widely accepted and widely used translation of the Bible.
The Old Testament contains 39 books, which includes historical narratives, poems, laws, and prophecies. The New Testament contains 27 books, which includes includes the four Gospels, the Acts of the Apostles, and various letters and prophecies.
Overall, the Bible text comprises approximately 786,122 words, and is organized into 31,102 verses. It includes a wide range of genres, including narratives, poems, letters, and prophecies, and covers a wide range of themes and perspectives. In order to prepare the text for analysis, the data is used to perform preprocessing steps such as tokenization, stop word removal, and stemming. This will allow us to focus on the core content of the text and to reduce the dimensionality of the data, making it more amenable to analysis with topic modeling algorithms.
Problem Statement
The Bible is a vast and complex text, comprising 66 books written by multiple authors over the course of many centuries. It includes a wide range of genres, themes, and perspectives, making it a challenging text to analyze and understand. Despite its importance as a cultural and historical document, the sheer size and complexity of the Bible make it difficult to gain a comprehensive understanding of its content and structure.
One way to address this challenge is through the use of topic modeling, a powerful tool for analyzing and organizing large collections of text data. By applying topic modeling algorithms to the Bible, it can uncover the underlying themes and topics present in the text, and explore how these themes and topics evolve over time. However, the application of topic modeling to the Bible is not without its challenges. The text is not a single, unified document, but rather a collection of texts with diverse content and structures, making it difficult to apply standard topic modeling techniques.
In this study, it aims to address the following problem: how will the usage of topic modeling helps to gain a deeper understanding of the themes and structure of the Bible, despite its complexity and diversity? To address this problem, it will apply Latent Dirichlet Allocation (LDA) modeling techniques to the Bible text, and analyze and interpret the resulting topic models. By doing so, it creates hope to contribute to the understanding of the Bible as a complex and multifaceted text, and to provide new insights into its themes and structures.
Methodology
The goal of this study is to apply topic modeling to the Bible in order to gain a deeper understanding of its themes and structure. To achieve this goal, this study will follow the following methodology:
1. Preprocessing: The first step in the analysis is to preprocess the Bible text in order to prepare it for analysis with topic modeling algorithms. This will involve tokenizing the text into individual words, removing stop words, lemmatization and stemming the words to their root form.
2. Topic Modeling: Once the text has been preprocessed, applying topic modeling algorithms, such as Latent Dirichlet Allocation (LDA), to the data in order to identify the underlying themes and topics present in the text. LDA is a probabilistic model that assumes that each document in the corpus is a mixture of a small number of latent topics, and that each word in the document is generated from one of these topics. By applying LDA to the preprocessed text, we can uncover the latent topics present in the text and explore how these topics evolve over time.
3. Analysis and Interpretation: Once the topic model has been generated, we will analyze and interpret the resulting topic distributions and identify the most dominant topics present in the text.
Overall, our methodology aims to provide a comprehensive analysis of the themes and structure of the Bible using state-of-the-art topic modeling techniques. By applying these techniques to the text, we hope to gain new insights into the underlying patterns and trends present in the Bible, and to contribute to the understanding of this important cultural and historical document.
Results and Discussion
1. Word Cloud — Word clouds, also known as tag clouds, are visual representations of the frequency of words in a text. They are typically created by assigning a font size to each word, with the size of the word proportional to its frequency in the text. Wordclouds can be useful for quickly visualizing the most common words in a text, and for identifying common themes and trends.
In the context of topic modeling, word clouds can be used to visualize the results of the preprocessing step, which typically involves removing duplicates, normalization, removing special characters, removing short words, tokenization, removing stop words and dropping rows having less than three tokens to transform the words into their root form.
For example, if a word cloud of the preprocessed text reveals that the most common words are related to a particular topic (e.g., faith, God, Jesus), this may suggest that this topic is a dominant theme in the text and should be captured by the topic model. On the other hand, if the word cloud reveals that the most common words are unrelated to any particular topic (e.g., the, and, but), this may indicate that the text is relatively homogenous and may not yield as many insights when analyzed with topic modeling algorithms.
2. Text Cleaning — Text cleaning, also known as preprocessing, is an important step in the process of topic modeling. It involves a series of techniques that are applied to the text in order to prepare it for analysis with topic modeling algorithms. These techniques typically include tokenization (breaking the text into individual words), stop word removal (removing common words that are not meaningful for the analysis), and lemmatization or stemming (reducing words to their root form).
The effects of text cleaning on the length of the text can vary depending on the specifics of the cleaning process and the characteristics of the text itself. In general, however, text cleaning tends to reduce the length of the text by removing words that are not meaningful for the analysis, such as stop words and words that are highly repetitive or infrequent. This can result in a shorter, more focused text that is more amenable to analysis with topic modeling algorithms.
In the case of the Bible, text cleaning may have a significant effect on the length of the text, particularly if the cleaning process involves the removal of a large number of stop words or the stemming of words to their root form. This could result in a shorter text that is more focused on the core content of the text and less cluttered with common words or repetitive phrases.
3. Most Frequent Words — The most frequent words after the preprocessing in the text may reveal important themes and trends present in the text. These words can provide valuable insights into the content and structure of the text, and can inform the topic modeling process.
3. Bigrams — Bigrams are pairs of words that appear together in a text. They can provide valuable insights into the content and structure of a text, and can inform the topic modeling process.
After preprocessing a text analyzing the bigrams present in the text can help to identify common phrases and themes present in the text. For example, if the bigrams after preprocessing the Bible text include words related to a particular theme (e.g., “faith in”, “son of”), this may suggest that this theme is a dominant aspect of the text and should be captured by the topic model.
Bigrams can also help to identify relationships between words, and can provide insights into the syntax and structure of the text. For example, analyzing the bigrams in the text may reveal patterns of co-occurrence between words, or may highlight words that are commonly used together in a particular context.
5. Creating Dictionary and Corpus — In the process of topic modeling, a dictionary and corpus are created from the preprocessed text in order to represent the text in a form that can be analyzed by topic modeling algorithms.
A dictionary is a list of all of the unique words in the text, along with their corresponding frequencies. It is typically created by tokenizing the text, removing stop words, and lemmatization/stemming the words to their root form. The dictionary allows the topic modeling algorithm to identify the most important words in the text, and to assign them to the appropriate topics.
A corpus is a collection of documents, in this case, the preprocessed text of the Bible. It is typically represented as a matrix, with each row representing a document (in this case, a verse of the Bible) and each column representing a word in the dictionary. The corpus allows the topic modeling algorithm to analyze the relationships between words and documents, and to identify the underlying themes and topics present in the text.
The dictionary and corpus created for the Bible text can provide valuable insights into the content and structure of the text. By analyzing the words and documents in the corpus, it will identify the most important words and themes in the text and can explore how these themes evolve over time. This can help to provide a deeper understanding of the Bible as a complex and multifaceted text and can inform the analysis and interpretation of the topic model.
6. Creating LDA Model and View the Topics — Once the dictionary and corpus have been created from the preprocessed text, the next step in the process of topic modeling is to build the model and view the topics. This is typically done using a topic modeling algorithm, such as Latent Dirichlet Allocation (LDA).
To build the LDA model, it is important to specify the number of topics which the model want to identify, as well as any other parameters that may be relevant for the analysis (e.g., the number of iterations, the learning rate). The model is then trained on the corpus, and the resulting topics are generated.
7. Evaluating Model Perplexity and Coherence Score — Once the LDA model has been built and the topics have been generated, it is important to evaluate the model’s performance in order to ensure that it is accurate and interpretable. There are a number of metrics that can be used to evaluate the model, including perplexity and coherence scores.
Perplexity is a measure of how well the model is able to predict the words in the text based on the topics it has identified. A lower perplexity score indicates that the model is able to accurately predict the words in the text, while a higher perplexity score indicates that the model is less accurate. A model with a low perplexity score is generally considered to be more interpretable and more effective at capturing the underlying structure of the text. A perplexity score of -7.963357900079297 is relatively low, which suggests that the model is able to accurately predict the words in the text based on the topics it has identified. This may indicate that the model is effective at capturing the underlying structure and themes of the text, and is likely to be interpretable and informative. However, it is important to note that the perplexity score should be interpreted in the context of the specific text and analysis being conducted. Factors such as the size and complexity of the text, the number of topics specified for the model, and the quality of the pre-processing may all affect the perplexity score. As such, it is important to consider the perplexity score in conjunction with other metrics, such as coherence scores, in order to get a complete picture of the model’s performance and interpretability.
Coherence score is a measure of how well the words within each topic are related to one another. A higher coherence score indicates that the words within each topic are more closely related and more coherent, while a lower coherence score indicates that the words are less related and less coherent. A model with a high coherence score is generally considered to be more interpretable and more effective at capturing the underlying structure of the text. A coherence score of 0.2997260932006047 is relatively low, which may suggest that the words within each topic are not as closely related as they could be, or that the topics themselves are not as coherent as they could be. This may indicate that the model is not as effective at capturing the underlying structure and themes of the text, or that the model is less interpretable and less informative. There is no one way to determine whether the coherence score is good or bad; The only rule is that to maximization this score. It is important to note, however, that the coherence score should be interpreted in the context of the specific text and analysis being conducted. Factors such as the size and complexity of the text, the number of topics specified for the model, and the quality of the preprocessing may all affect the coherence score. As such, it is important to consider the coherence score in conjunction with other metrics, such as perplexity scores, in order to get a complete picture of the model’s performance and interpretability.
The perplexity and coherence scores for the LDA model of the Bible text can provide valuable insights into the model’s performance and interpretability. By analyzing these scores, it is possible to assess the accuracy and coherence of the model, and can identify any areas where the model may be less effective or less interpretable. This can inform the analysis and interpretation of the model, and can help to gain a deeper understanding of the Bible as a complex and multifaceted text.
8. Intertopic Distance Map (pyLDAvis.gensim_model) — An intertopic distance map, also known as a multidimensional scaling (MDS) plot, is a visualization tool that can be used to explore the relationships between topics identified by a topic modeling algorithm. It is typically created by calculating the distance between pairs of topics, and then projecting these distances onto a two-dimensional plot.
An intertopic distance map can provide valuable insights into the structure and relationships between the topics identified by the topic modeling algorithm. For example, if the topics are plotted close together on the map, this may suggest that they are related or similar, while if the topics are plotted farther apart, this may suggest that they are less related or more distinct and the visualizations may reveal clusters of related topics, or may highlight the most important words for each topic.
In the context of the Bible, an intertopic distance map could be used to explore the relationships between the topics identified by the topic modeling algorithm, and to gain a better understanding of the structure and themes of the text. By analysing the positions of the topics on the map, it can identify clusters of related topics, or can explore how the topics evolve over time. This can help to provide a deeper understanding of the Bible as a complex and multifaceted text, and can inform the analysis and interpretation of the model.
9. Finding the Optimal number of Topics — Finding the optimal number of topics for a topic modeling analysis is an important aspect of the analysis process, as it determines the granularity of the resulting topics. Choosing the right number of topics is crucial for ensuring that the model is effective at capturing the underlying structure and themes of the text, and is interpretable and informative.
There are a number of approaches that can be used to find the optimal number of topics for a topic modeling analysis. One common approach is to use a measure such as the perplexity score or the coherence score to evaluate the model’s performance for a range of different numbers of topics. By comparing the scores for different numbers of topics, researchers can identify the number of topics that yields the best balance between accuracy and interpretability.
Another approach is to use visualizations of the topics, such as word clouds or intertopic distance maps, to explore the structure and relationships between the topics. By analyzing the visualizations, researchers can identify the number of topics that provides the most coherent and interpretable set of topics.
In the context of the Bible, finding the optimal number of topics may involve a combination of these approaches. By evaluating the performance of the model using metrics such as perplexity and coherence scores, and by analyzing visualizations of the topics, researchers can identify the number of topics that best captures the underlying structure and themes of the text, and is most interpretable and informative.
10. Dominant Topics - The dominant topics identified by a topic modeling algorithm are the topics that are most prevalent or important in the text. They reflect the underlying structure and themes of the text, and can provide valuable insights into the content and organization of the text.
The dominant topics identified by a topic modeling algorithm can provide valuable insights into the structure and themes of the Bible. By analyzing the most important words for each topic, researchers can identify the dominant themes and trends present in the text, and can explore how these themes evolve over time. This can help to provide a deeper understanding of the Bible as a complex and multifaceted text, and can inform the analysis and interpretation of the model.
It is important to note, however, that the dominant topics identified by the topic modeling algorithm are not necessarily exhaustive or definitive. The model may identify some important themes and trends in the text, but it is likely to miss others. As such, it is important to interpret the dominant topics in the context of the specific text and analysis being conducted, and to consider other sources of information and evidence when interpreting the results of the model.
11. Wordcloud of Top N words in each topic — Interpreting a word cloud of the top N words in each topic can provide valuable insights into the content and structure of the text, and can inform the analysis and interpretation of the topic model. By analyzing the words in the word cloud, researchers can identify the dominant themes and trends present in the text, and can explore how these themes evolve over time.
Relevance and Future Scope of LDA Topic Modelling of Bible
Latent Dirichlet Allocation (LDA) is a powerful and widely used topic modeling algorithm that can be used to analyze and interpret large, complex texts such as the Bible. By identifying the underlying themes and patterns in the text, LDA can provide valuable insights into the structure and content of the text, and can inform the analysis and interpretation of the text.
There is a significant amount of ongoing research in the field of topic modeling, and LDA is an active area of study. In the future, it is likely that advances in machine learning and natural language processing techniques will enable more sophisticated and effective topic modeling algorithms. These algorithms may be able to identify more subtle and nuanced themes in the text, and may be able to capture more complex relationships between words and documents.
In terms of the relevance of LDA topic modeling to the Bible, the algorithm has the potential to provide valuable insights into the structure and content of the text, and to inform the analysis and interpretation of the text. By identifying the dominant themes and trends present in the text, researchers can gain a deeper understanding of the Bible as a complex and multifaceted text, and can explore how these themes evolve over time. This can inform the analysis and interpretation of the text, and can provide a more comprehensive understanding of the Bible’s content and structure.
References
1. Chandra, R., & Ranjan, M. (2022, September 1). Artificial intelligence for topic modelling in Hindu philosophy: Mapping themes between the Upanishads and the Bhagavad Gita. PLOS ONE, 17(9), e0273476. https://doi.org/10.1371/journal.pone.0273476
2. Verma, M. (2017, June 15). Lexical Analysis of Religious Texts using Text Mining and Machine Learning Tools. International Journal of Computer Applications, 168(8), 39–45. https://doi.org/10.5120/ijca2017914486
3. Garbhapu, V. K., & Bodapati, P. (2022, June 30). Extractive Summarization of Bible Data using Topic Modeling. International Journal of Engineering Trends and Technology, 70(6), 79–89. https://doi.org/10.14445/22315381/ijett-v70i6p210
4. McDonald, Daniel. (2014). A Text Mining Analysis of Religious Texts. The Journal of Business Inquiry. 13. 27–47.
5. Coeckelbergs, & Hooland. (n.d.). Modeling the Hebrew Bible : Potential of Topic Modeling Techniques for Semantic Annotation and Historical Analysis. Semantic Web for Scientific Heritage.
6. Kim, S., Lee, N., & King, P. E. (2020, January 30). Dimensions of Religion and Spirituality: A Longitudinal Topic Modeling Approach. Journal for the Scientific Study of Religion, 59(1), 62–83. https://doi.org/10.1111/jssr.12639
7. Tavares, L. (2020, January 5). NLP of Bible Chapters and Books — Similarity and Clustering with Python. Medium. Retrieved December 21, 2022, from https://medium.com/analytics-vidhya/nlp-of-bible-chapters-and-books-similarity-and-clustering-with-python-69c9073251e
