TF-IDF Vectorization
TF-IDF (Term Frequency-Inverse Document Frequency) is a metric that quantifies the value of a term in a document in relation to a group of documents and is used in natural language processing. It is frequently employed in text mining, information retrieval, and text analysis.
- Term Frequency (TF) – It measures how frequently a term appears in a document.
- Inverse Document Frequency(IDF) – It calculates the importance of a term in a collection of documents by considering how often it appears across the whole collection.
- TF-IDF Score: It combines the TF and IDF to assess the importance of a term in a specific document.
Syntax : TF – IDF(t , d, D) = TF (t, d) * IDF( t, D)
Where,
t =term in the document
d = A document
D = collection of the documents
Now let’s start with the implementation
Python3
# Function to create TF-IDF vectors from a list of documents def create_tfidf_vectors(docs): return TfidfVectorizer().fit_transform(docs).toarray() |
In this code, the function create_tfidf_vectors takes a list of text documents, uses sklearn’s ‘Tfidfvectorizer’ to calculate TF-IDF vectors for those documents, and return the TF-IDF vecotrs as a numpy array.
Plagiarism Detection using Python
In this article, we are going to learn how to check plagiarism using Python.
Plagiarism: Plagiarism basically refers to cheating. It means stealing someone’s else work, ideas, or information from the resources without providing the necessary credits to the author. For example, copying text from different resources from word to word without mentioning any quotation marks.
Table of Content
- What is Plagiarism detection?
- Importing Libraries
- Listing and Reading Files
- TF-IDF Vectorization
- Calculating Cosine Similarity
- Creating Document-vector Pairs
- Checking Plagiarism
- Word Cloud Visualization
- Conclusion
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