Application of Similarity Recognition in Python Text Processing

In the fields of natural language processing and text analysis, string matching and recognition of text similarity are common issues. Whether it is text data cleaning, text classification, or text retrieval, it is crucial to efficiently match strings or recognize similarities between texts. Python provides various libraries and tools to implement these functions, which can help developers quickly handle string matching and similarity recognition issues.

Application scenarios

Cosine similarity is a similarity measure used to calculate the angle between two vectors, particularly suitable for processing text data. After converting the text into a vector, cosine similarity can be used to calculate its similarity.

usescikit-learnCalculate cosine similarity

First, installscikit-learnLibrary:

pip install scikit-learn

The following is the usagescikit-learnExample code for calculating cosine similarity of text:

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity     #Define two texts     
text1 = "I love Python programming"
text2 = "Python programming is fun"     #Convert text to vectors     
vectorizer = CountVectorizer().fit_transform([text1, text2])
vectors = vectorizer.toarray()     #Calculate cosine similarity     
cos_sim =…