Are you tired of spending hours comparing text documents for similarities?
In this tutorial, we’ll walk you through the steps to create a software program that calculates the similarity percentage between two texts. No more manually comparing documents or hiring expensive consultants just sit back and let our code do the heavy lifting for you!
Step 1: Setting up the Environment
To kick things off, make sure Python is installed on your system (if it’s not already). You can download the latest version from their official website. Once that’s done, we’ll need to install NLTK a popular library for natural language processing in Python. To do this, open up your terminal and run:
# This line installs the NLTK library using the pip package manager
pip install nltk
# This line imports the necessary modules from the NLTK library
from nltk import download
# This line downloads the necessary data for NLTK to function properly
download('all')
# This line creates a variable to store the path to the NLTK data
nltk_data_path="/usr/local/share/nltk_data"
export NLTK_DATA=$nltk_data_path
# This line checks if the NLTK data has been successfully downloaded and sets a variable to store the result
nltk_data_downloaded=$(python -c "import nltk; print(nltk.data.find('corpora/stopwords'))")
# This line checks if the NLTK data has been successfully downloaded and sets a variable to store the result
if [ "$nltk_data_downloaded" == "$nltk_data_path/corpora/stopwords" ]; then
# This line prints a message indicating that the NLTK data has been successfully downloaded
echo "NLTK data has been successfully downloaded."
else
# This line prints an error message if the NLTK data has not been downloaded
echo "Error: NLTK data has not been downloaded."
fi
Step 2: Preprocessing the Text Data
Before we can calculate similarity scores, we need to preprocess our text data. This involves cleaning it up by removing punctuation marks, converting all words to lowercase, and splitting them into individual tokens (words). Here’s an example of how you might do this using NLTK:
# Import necessary libraries
import nltk # Import the Natural Language Toolkit library
from nltk.corpus import stopwords # Import the stopwords corpus from NLTK
from nltk.stem.porter import PorterStemmer # Import the PorterStemmer from NLTK
def preprocess_text(text):
# Remove punctuation marks and convert to lowercase
text = re.sub(r'[^\w\s]', '', text) # Use regular expressions to remove all non-alphanumeric characters
text = text.lower() # Convert all characters to lowercase
# Split into individual tokens (words)
words = nltk.word_tokenize(text) # Use NLTK's word_tokenize function to split the text into individual words
# Remove stopwords and stem the remaining words
stop_words = set(stopwords.words('english')) # Create a set of stopwords from the English language
ps = PorterStemmer() # Initialize the PorterStemmer
filtered_words = [] # Create an empty list to store the filtered words
for word in words:
if word not in stop_words: # Check if the word is not a stopword
filtered_words.append(ps.stem(word)) # Stem the word and add it to the filtered_words list
return ' '.join(filtered_words) # Join the filtered_words list into a string separated by spaces
Step 3: Calculating Similarity Scores
Now that we’ve preprocessed our text data, it’s time to calculate similarity scores using cosine similarity a widely used metric in natural language processing. Here’s an example of how you might do this:
# Import necessary libraries
from nltk.corpus import stopwords # Import stopwords from nltk corpus
from nltk.stem.porter import PorterStemmer # Import PorterStemmer from nltk stem
import math # Import math library for mathematical operations
# Define function to calculate similarity score between two texts
def calculate_similarity(text1, text2):
# Preprocess the input texts using our preprocessing function from Step 2
text1 = preprocess_text(text1) # Preprocess text1 using preprocess_text function
text2 = preprocess_text(text2) # Preprocess text2 using preprocess_text function
# Convert both texts into a list of word frequencies (vectorization)
words1 = nltk.word_counts(nltk.sentiment.SentimentIntensityAnalyzer().polarity_scores(text1)) # Convert text1 into a list of word frequencies using sentiment analysis
words2 = nltk.word_counts(nltk.sentiment.SentimentIntensityAnalyzer().polarity_scores(text2)) # Convert text2 into a list of word frequencies using sentiment analysis
# Calculate the cosine similarity between the two vectors (lists of word frequencies)
numerator = 0 # Initialize numerator variable to 0
denominator1 = math.sqrt(sum([words1[word]**2 for word in words1])) # Calculate denominator1 using square root of sum of squared word frequencies in words1
denominator2 = math.sqrt(sum([words2[word]**2 for word in words2])) # Calculate denominator2 using square root of sum of squared word frequencies in words2
# Loop through words and frequencies in words1
for word, freq in words1.items():
if word in words2: # Check if word is also in words2
numerator += (freq * words2[word]) # Add product of frequencies to numerator
similarity_score = round((numerator / (denominator1 * denominator2)), 4) # Calculate similarity score by dividing numerator by product of denominators and rounding to 4 decimal places
return similarity_score # Return similarity score as output
Step 4: Creating a User Interface
Now that we’ve created our text similarity calculator, let’s create a user interface to make it more accessible. Here’s an example of how you might do this using Python’s built-in `input()` function and some basic string manipulation:
# Creating a function called "main" to serve as the entry point of the program
def main():
# Printing a header for the program
print("Text Similarity Calculator")
print("==========================")
# Prompting the user to enter the first text and storing it in a variable called "text1"
text1 = input("Enter the first text:\n").strip()
# Prompting the user to enter the second text and storing it in a variable called "text2"
text2 = input("Enter the second text:\n").strip()
# Calling the "calculate_similarity" function and passing in the two texts as arguments
similarity_score = calculate_similarity(text1, text2)
# Multiplying the similarity score by 100 and rounding it to 2 decimal places to get the similarity percentage
similarity_percentage = round((similarity_score * 100), 2)
# Printing the similarity percentage to the user
print(f"\nSimilarity Percentage: {similarity_percentage}%")
# Calling the "main" function to start the program
main()
Step 5: Running the Program
Finally, let’s run our program and test it out! Save your code as a Python file (e.g., `text_similarity.py`) in a directory of your choice. Open up your terminal or command prompt and navigate to that directory. Run the following command:
#!/bin/bash # This line specifies the interpreter to be used for executing the script
# Step 5: Running the Program
# Finally, let's run our program and test it out! Save your code as a Python file (e.g., `text_similarity.py`) in a directory of your choice. Open up your terminal or command prompt and navigate to that directory. Run the following command:
# Here is the script:
python text_similarity.py # This line executes the Python script named "text_similarity.py"
You’ve just created your very own Text Similarity Calculator using Python no more manual comparisons for you!