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Python | Measure similarity between two sentences using cosine similarity

  • Last Updated : 10 Jul, 2020

Cosine similarity is a measure of similarity between two non-zero vectors of an inner product space that measures the cosine of the angle between them.
Similarity = (A.B) / (||A||.||B||) where A and B are vectors.

Cosine similarity and nltk toolkit module are used in this program. To execute this program nltk must be installed in your system. In order to install nltk module follow the steps below –

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1. Open terminal(Linux).
2. sudo pip3 install nltk
3. python3
4. import nltk
5. nltk.download(‘all’)

Functions used:



nltk.tokenize: It is used for tokenization. Tokenization is the process by which big quantity of text is divided into smaller parts called tokens. word_tokenize(X) split the given sentence X into words and return list.

nltk.corpus: In this program, it is used to get a list of stopwords. A stop word is a commonly used word (such as “the”, “a”, “an”, “in”).

Below is the Python implementation –




# Program to measure the similarity between 
# two sentences using cosine similarity.
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
  
# X = input("Enter first string: ").lower()
# Y = input("Enter second string: ").lower()
X ="I love horror movies"
Y ="Lights out is a horror movie"
  
# tokenization
X_list = word_tokenize(X) 
Y_list = word_tokenize(Y)
  
# sw contains the list of stopwords
sw = stopwords.words('english'
l1 =[];l2 =[]
  
# remove stop words from the string
X_set = {w for w in X_list if not w in sw} 
Y_set = {w for w in Y_list if not w in sw}
  
# form a set containing keywords of both strings 
rvector = X_set.union(Y_set) 
for w in rvector:
    if w in X_set: l1.append(1) # create a vector
    else: l1.append(0)
    if w in Y_set: l2.append(1)
    else: l2.append(0)
c = 0
  
# cosine formula 
for i in range(len(rvector)):
        c+= l1[i]*l2[i]
cosine = c / float((sum(l1)*sum(l2))**0.5)
print("similarity: ", cosine)

Output:

similarity:  0.2886751345948129



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