Processing Text Data for Bayesian Inference with Python

Bayesian inference is a method of statistical analysis that allows us to update probability estimates as new data arrives. In the realm of Natural Language Processing (NLP), it is often used in spam detection, sentiment analysis, and more. Let's explore the initial steps of preprocessing text data for Bayesian inference.

---

1. Convert Text to Lowercase: To ensure consistency, we convert all text data to lowercase using Python's lower() method.

documents = ['Hello, how are you!',
             'Win money, win from home.',
             'Call me now.',
             'Hello, Call hello you tomorrow?']

lower_case_documents = [doc.lower() for doc in documents]
print(lower_case_documents)

2. Remove Punctuation: Eliminating punctuation from text aids in achieving more accurate word frequency counts.

sans_punctuation_documents = []
import string

for doc in lower_case_documents:
    sans_punctuation_documents.append(doc.translate(str.maketrans('', '', string.punctuation)))
print(sans_punctuation_documents)

 

3. Tokenization: Tokenization involves splitting text data into individual words or tokens. This helps in the later stages of vectorization and feature extraction.

preprocessed_documents = [doc.split() for doc in sans_punctuation_documents]
print(preprocessed_documents)

4. Count Frequencies: To perform Bayesian inference, we often need word frequency counts. Here's how to achieve that:

from collections import Counter

frequency_list = [Counter(doc) for doc in preprocessed_documents]
print(frequency_list)

5. Vectorization using sklearn: The sklearn library provides a powerful tool called CountVectorizer which allows us to convert a collection of text documents to a matrix of token counts.

from sklearn.feature_extraction.text import CountVectorizer

count_vector = CountVectorizer()
doc_array = count_vector.fit_transform(sans_punctuation_documents).toarray()

import pandas as pd
frequency_matrix = pd.DataFrame(data=doc_array, columns=count_vector.get_feature_names())
print(frequency_matrix)

---

Preprocessing is a fundamental step in NLP. These initial steps of converting text to lowercase, removing punctuation, tokenizing, and counting frequencies lay the foundation for more advanced procedures and analyses like Bayesian inference.

---