Extracting Acronyms and Finding Hidden Messages from Text

article

Introduction

This documentation explains a Python script that enables users to extract acronyms from a given text and discover potential hidden messages within those acronyms. The script utilizes the Natural Language Toolkit (NLTK) library and itertools module to carry out the required operations. It also validates the extracted acronyms against an English word set, which may provide meaningful words forming the hidden message.

Requirements

Before running the script, ensure you have the following prerequisites installed:

1. Python: The script is written in Python, so make sure you have a compatible Python version (e.g., Python 3.x) installed on your system.
2. NLTK: Install the Natural Language Toolkit library to access the English word dictionary.
3. Text File: Prepare a text file containing the article you want to analyze. Specify the file path correctly in the script.

Full code:

import nltk
from nltk.corpus import words
from itertools import permutations

def extract_acronym_from_text(original_text):
    words = original_text.split()
    acronym_dict = {}

    for i in range(len(words)):
        if i == 0 or (i > 0 and words[i - 1][-1] in ".!?"):
            word = words[i]
            acronym = word[0].upper()
            acronym_dict[acronym] = word

    return acronym_dict

def find_possible_permutations(acronym_dict):
    possible_permutations = []
    for k in range(1, len(acronym_dict) + 1):
        for permutation in permutations(acronym_dict.keys(), k):
            formed_word = "".join(permutation)
            possible_permutations.append(formed_word)

    return possible_permutations

def load_english_word_set():
    nltk.download("words")
    return set(words.words())

def is_word_in_english_dictionary(word, english_word_set):
    return word.lower() in english_word_set

def read_text_from_file(file_path):
    with open(file_path, 'r') as file:
        return file.read()

# File path to your article.txt file
file_path = "article.txt"

# Load the article text from the file
article_text = read_text_from_file(file_path)
acronym_dict = extract_acronym_from_text(article_text)
possible_permutations = find_possible_permutations(acronym_dict)

english_word_set = load_english_word_set()

print("Article Content:")
print(article_text)

print("\nEnglish:")
for permutation in possible_permutations:
    if is_word_in_english_dictionary(permutation, english_word_set):
        print(permutation)

Script Functionality

The Python script consists of five main functions:

1. extract_acronym_from_text(original_text): This function extracts acronyms from the provided text. It identifies words starting with a capital letter immediately after a period, exclamation mark, or question mark.
2. find_possible_permutations(acronym_dict): This function generates all possible permutations of the acronyms extracted from the text.
3. load_english_word_set(): The function downloads the English word set from the NLTK corpus.
4. is_word_in_english_dictionary(word, english_word_set): This function checks if a given word exists in the English word set. It performs a case-insensitive search to find matches.
5. read_text_from_file(file_path): This function reads the article content from the specified text file.

Code Execution

Before running the script, replace "article.txt" in the file_path variable with the path to your article text file.

When executed, the script performs the following steps:

1. Reads the article content from the specified file.
2. Extracts acronyms from the text.
3. Generates all possible permutations of the extracted acronyms.
4. Downloads the English word set.
5. Checks each permutation against the English word set.
6. Prints the original article content and lists the valid English words formed by the acronyms.

Conclusion

The provided Python script offers a simple and efficient way to extract acronyms from a given article text and validates those acronyms against an English word set. This tool can be useful for analyzing text with potential acronyms and understanding the valid words formed by those acronyms. Feel free to modify and extend the script to suit your specific needs. Happy coding!

My Github:

GitHub - jackGetDev/HiddenMessageUnveilerAcronym