You know the drill, you have two texts, maybe they’re emails or articles, and you want to see how similar they are.

To kick things off, lets talk about what we need for this project. We will be using the Natural Language Toolkit (NLTK) library which is a powerful tool for working with human language data in Python. It has many features and functions that can help us analyze and manipulate text data. So, make sure you have NLTK installed on your machine before we proceed.

Now let’s get started! We will be using the cosine similarity algorithm to calculate the similarity between two texts. This is a popular method for measuring similarity in vector spaces which are commonly used in text analysis and information retrieval. The cosine similarity measures how closely two vectors point in the same direction, with values ranging from 0 (no similarity) to 1 (identical).

Here’s an example of what our Python script might look like:

# Import necessary libraries
import nltk # Importing the Natural Language Toolkit library
from nltk.corpus import stopwords # Importing the stopwords corpus from NLTK
from nltk.stem.porter import PorterStemmer # Importing the PorterStemmer from NLTK
from sklearn.metrics.pairwise import cosine_similarity # Importing the cosine_similarity function from sklearn's pairwise module

# Load the texts we want to compare
text1 = "This is a sample text for demonstration purposes."
text2 = "The purpose of this text is also for demonstration."

# Preprocess the texts by removing stopwords and stemming words.
stop_words = set(stopwords.words('english')) # Creating a set of stopwords from the English language
stemmer = PorterStemmer() # Initializing the PorterStemmer
def preprocess(sentence):
    sentence = nltk.word_tokenize(sentence) # Tokenizing the sentence into individual words
    sentence = [w for w in sentence if not w in stop_words] # Removing stopwords from the sentence
    sentence = [stemmer.stem(word) for word in sentence] # Stemming each word in the sentence
    return ' '.join(sentence) # Joining the stemmed words back into a sentence

# Preprocess the texts and convert them into vectors using TF-IDF (Term Frequency Inverse Document Frequency).
from sklearn.feature_extraction.text import TfidfVectorizer # Importing the TfidfVectorizer from sklearn
vectorizer = TfidfVectorizer() # Initializing the TfidfVectorizer
X = vectorizer.fit_transform([preprocess(text1), preprocess(text2)]) # Preprocessing the texts and converting them into vectors using TF-IDF

# Calculate the cosine similarity between the two vectors using sklearn's pairwise module.
similarity = cosine_similarity(X)[0][1] # Calculating the cosine similarity between the two vectors
print("The similarity score is: ", round(similarity, 4)) # Printing the similarity score rounded to 4 decimal places

Now let me explain what we did here. First, we imported the necessary libraries and functions from NLTK and sklearn. We then loaded our two sample texts and preprocessed them by removing stopwords and stemming words using a function that we defined earlier. This is an important step in text analysis as it helps to remove common words like “the” or “and”, which can skew the results of similarity calculations.

Next, we converted our preprocessed texts into vectors using TF-IDF (Term Frequency Inverse Document Frequency), a popular technique for converting text data into numerical features that can be used in machine learning algorithms. This is done by creating a vectorizer object and fitting it to the preprocessed texts.

Finally, we calculated the cosine similarity between our two vectors using sklearn’s pairwise module. The result is stored in the ‘similarity’ variable which we then printed out with 4 decimal places of precision.

And that’s it! You now have a Python text similarity calculator at your fingertips. This can be useful for many applications such as plagiarism detection, content analysis or even just comparing two articles to see how closely related they are. So give it a try! Your texts will thank you for it.

Later!