Text Mining

Text mining refers to the process of transforming large quantities of text data into structured databases that can be analyzed for insights. These databases can then be scrutinized using various data science methods, particularly NLP techniques, to derive insights and respond to research inquiries regarding the text.

This presents vast analytical possibilities across many qualitative disciplines, such as philosophy, archaeology, English studies, gender studies, music, finance, and linguistics. For instance, one could analyze mentions of “inflation” in Federal Reserve statements to gauge economic sentiment, or examine sentence structures and rhyming schemes in Shakespeare’s works. In our case, we focus on revealing trends and insights in ancient writings.

Challenges with Analyzing Ancient Texts

Analyzing ancient texts presents distinct challenges compared to other data sources. Translation can be particularly challenging due to the intricacies of the ancient language, where nuanced meanings can significantly alter the final interpretation.

Moreover, while advancements in technology have simplified analysis, human expertise remains crucial for many facets, including feature selection.

Additionally, ancient texts are often incomplete, with missing characters, words, or sections due to the deterioration of original materials. This poses substantial hurdles for data science techniques. However, recent advancements (2022) in Deep Neural Networks (DNN) have shown promise in restoring ancient texts and predicting missing elements. For those interested, a study published in March 2022 discusses the use of a DNN named Ithica for restoring and attributing ancient writings.

Example in Python

Let’s explore a straightforward example in Python to demonstrate how some NLP techniques can be applied to ancient texts. If you want to follow along, launch your preferred Python IDE! I recommend using one of the following ancient text databases supported by universities:

1. Brown University Library Full-Text Database of Classics
2. Harvard University Ancient Text Resources
3. University of California, Berkeley Library Indexes and Full-Text Databases for Classics
4. The University of Manchester Library Classics and Ancient History Databases
5. Penn State University Libraries Ancient Greek Text Database

import nltk from nltk.tokenize import word_tokenize from nltk.corpus import stopwords from nltk.stem import WordNetLemmatizer import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer

# Load the text data with open('ancient_text.txt', 'r') as f:

ancient_text = f.read()

# Tokenize the text and remove stop words tokens = word_tokenize(ancient_text.lower()) stop_words = set(stopwords.words('english')) filtered_tokens = [token for token in tokens if token not in stop_words]

# Lemmatize the tokens lemmatizer = WordNetLemmatizer() lemmatized_tokens = [lemmatizer.lemmatize(token) for token in filtered_tokens]

# Create a document-term matrix vectorizer = TfidfVectorizer() dtm = vectorizer.fit_transform([ancient_text])

# Convert the dtm to a pandas dataframe df_dtm = pd.DataFrame(dtm.toarray(), columns=vectorizer.get_feature_names_out())

# Print the top 10 most common words in the text print(df_dtm.sum().sort_values(ascending=False).head(10))

To begin, I load an ancient text from a file and utilize the Natural Language Toolkit (NLTK) library in Python for tokenization, which breaks the text into individual words. NLTK is user-friendly and comprehensive for various NLP tasks. If you want to see this library in action, check out my article where I analyze Kendrick Lamar’s entire discography.

Next, I filter out stop words, which are common terms that add little meaning. I then lemmatize the remaining tokens, reducing each word to its root form to streamline data dimensionality and enhance analytical accuracy.

With the preprocessed text, I employ the TF-IDF (term frequency-inverse document frequency) vectorizer to construct a document-term matrix—a mathematical representation of the text. This matrix assigns weights to each word based on its frequency and importance in conveying the text's overall meaning.

Finally, I transform the document-term matrix into a pandas dataframe and display the ten most frequently occurring words. This is merely an introductory example; numerous other NLP techniques can be applied to ancient texts.

Natural Language Processing Techniques

Additional techniques applicable to the processed text include:

1. Sentiment Analysis
2. Named Entity Recognition
3. Automatic Summarization
4. Machine Translation
5. Neural Networks
6. Topic Modeling

Sentiment analysis involves assigning sentiment scores to each word and summarizing the overall sentiment of the text or analyzing it by specific segments. The NTLK package can facilitate this in Python. For an example, refer to my article on analyzing Kendrick Lamar’s discography.

Named entity recognition is akin to sentiment analysis, involving the tagging of various entities, whether they be organizations, individuals, monetary units, or locations, along with a count of their occurrences in the text. This information can be used to observe trends, rank the significance of different entities, and analyze the language and structure of ancient texts.

Automatic summarization simplifies complex jargon into more digestible and understandable terms. This process can be intricate, and many online services offer this capability.

Machine translation is self-explanatory but particularly challenging for ancient texts due to often missing characters and words, necessitating human oversight for accurate translations.

Neural networks represent a more sophisticated NLP approach for text analysis. Again, for further insights, see the March 2022 study on how the DNN Ithica was employed to restore and attribute ancient texts.

Topic modeling is an unsupervised NLP method that utilizes AI to categorize and cluster text based on shared topics or features defined by the researcher. For example, one might use topic modeling to trace themes like love or violence in a particular author's work.

The influence of text mining on ancient writings is profound, as it enables the discovery of patterns and information that would remain undetected through human analysis alone. By leveraging natural language processing techniques on historical documents, we can delve into the content and context of these texts, yielding insights into the social, cultural, and political milieu of their time.