Word Cloud Visualization
Now , let’s represent each document with a wordcloud.
Wordcloud for John.txt
Python3
# Function to generate a word cloud for a document def generate_word_cloud(document_text, filename): # Create a word cloud from the document text wordcloud = WordCloud(width=800, height=400).generate(document_text) # Create a figure to display the word cloud plt.figure(figsize=(8, 4)) # Display the word cloud as an image with bilinear interpolation plt.imshow(wordcloud, interpolation='bilinear') # Set the title of the word cloud figure to include the filename plt.title(f'Word Cloud for {filename}') # Turn off axis labels and ticks plt.axis('off') # Show the word cloud visualization plt.show() # Find plagiarism among student documents and store the results plagiarism_results = find_plagiarism() # Iterate through plagiarism results for result in plagiarism_results: # Check if the similarity score is greater than or equal to 0.5 (adjust as needed) if result[2] >= 0.5: # Generate and display a word cloud for the document with similarity above the threshold generate_word_cloud(open(result[0]).read(), result[0]) |
Output:
Here, in the code, it combines the plagiarism detection with word cloud generation , visually representing documents with high similarity scores through the word cloud visualizations. Here, we are representing the word cloud for john.txt document.
Wordcloud for fatma.txt
Let’s build another word cloud for second document used to build the model.
Python3
# Specify the target document filename target_document = "fatma.txt" # Iterate through pairs of filenames and document vectors for filename, document_vector in doc_filename_pairs: # Check if the current filename matches the target_document if filename == target_document: # Generate a word cloud for the target document generate_word_cloud(open(filename).read(), filename) |
Output:
This code iterates through a list of document pairs, checking if a specific document(‘target_document’) is found, and if so, generates the word cloud for that document. T
Wordcloud for Juma.txt
Let’s build another word cloud for third document used to build the model.
Python3
# Specify the target document filename target_document = "juma.txt" # Iterate through pairs of filenames and document vectors for filename, document_vector in doc_filename_pairs: # Check if the current filename matches the target_document if filename == target_document: # Generate a word cloud for the target document generate_word_cloud(open(filename).read(), filename) |
Output:
This code searches for a specific document (‘juma.txt’ ) in the list of the document pairs(‘doc_filename_pairs’). If it finds a match, it generates a word cloud for that document, visually representing its content using the ‘generate_word_cloud’ function.
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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