Hi guys, In this tutorial, we're going to learn how to Make a Plagiarism Detector in Python using machine learning techniques such as word2vec and cosine similarity in just a few lines of code.
Once finished our plagiarism detector will be capable of loading a student’s assignment from files and then compute the similarity to determine if students copied each other.
pip install -U scikit-learn Exit fullscreen mode
We all know that computers can only understand 0s and 1s, and for us to perform some computation on textual data we need a way to convert the text into numbers.
The process of converting the textual data into an array of numbers is generally known as word embedding. The vectorization of textual data to vectors is not a random process instead it follows certain algorithms resulting in words being represented as a position in space. we going to use scikit-learn built-in features to do this.
Here we gonna use the basic concept of vector, dot product to determine how closely two texts are similar by computing the value of cosine similarity between vectors representations of student’s text assignments. Also, you need to have sample text documents on the student’s assignments which we gonna use in testing our model. The text files need to be in the same directory with your script with an extension of .txt, If you wanna use sample textfiles I used for this tutorial download here The project directory should look like this
. ├── app.py ├── fatma.txt ├── image.png ├── john.txt └── juma.txt Exit fullscreen mode
import os from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity Exit fullscreen mode
we gonna use OS Module in loading paths of textfiles and then TfidfVectorizer to perform word embedding on our textual data and cosine similarity to compute the plagiarism.
We are going to use concepts of a list comprehension to load all the path textfiles on our project directory as shown below.
student_files = [doc for doc in os.listdir() if doc.endswith('.txt')] Exit fullscreen mode
We need to create two lambda functions, one to convert the text to arrays of numbers and the other one to compute the similarity between them.
vectorize = lambda Text: TfidfVectorizer().fit_transform(Text).toarray() similarity = lambda doc1, doc2: cosine_similarity([doc1, doc2]) Exit fullscreen mode
adding the below two lines to vectorize the loaded student files.
vectors = vectorize(student_notes) s_vectors = list(zip(student_files, vectors)) Exit fullscreen mode
Below is the main function of our script responsible for managing the whole process of computing the similarity among students.
def check_plagiarism(): plagiarism_results = set() global s_vectors for student_a, text_vector_a in s_vectors: new_vectors =s_vectors.copy() current_index = new_vectors.index((student_a, text_vector_a)) del new_vectors[current_index] for student_b , text_vector_b in new_vectors: sim_score = similarity(text_vector_a, text_vector_b)[0][1] student_pair = sorted((student_a, student_b)) score = (student_pair[0], student_pair[1],sim_score) plagiarism_results.add(score) return plagiarism_results Let’s print plagiarism results for data in check_plagiarism(): print(data) Exit fullscreen mode
When you compile down all the above concepts, you get the below full scripts ready to ** to detect plagiarism** among student's assignments.
import os from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.metrics.pairwise import cosine_similarity student_files = [doc for doc in os.listdir() if doc.endswith('.txt')] student_notes =[open(File).read() for File in student_files] vectorize = lambda Text: TfidfVectorizer().fit_transform(Text).toarray() similarity = lambda doc1, doc2: cosine_similarity([doc1, doc2]) vectors = vectorize(student_notes) s_vectors = list(zip(student_files, vectors)) def check_plagiarism(): plagiarism_results = set() global s_vectors for student_a, text_vector_a in s_vectors: new_vectors =s_vectors.copy() current_index = new_vectors.index((student_a, text_vector_a)) del new_vectors[current_index] for student_b , text_vector_b in new_vectors: sim_score = similarity(text_vector_a, text_vector_b)[0][1] student_pair = sorted((student_a, student_b)) score = (student_pair[0], student_pair[1],sim_score) plagiarism_results.add(score) return plagiarism_results for data in check_plagiarism(): print(data) Exit fullscreen mode
Once you run the above app.py the out will look as shown below
$ python app.py #__________RESULT ___________ ('john.txt', 'juma.txt', 0.5465972177348937) ('fatma.txt', 'john.txt', 0.14806887549598566) ('fatma.txt', 'juma.txt', 0.18643448370323362) Exit fullscreen mode
Congratulations you have just made your own Plagiarism Detector in Python, Now share it with your fellow peers, press Tweet now to share it.
In case of any comment, suggestion, or difficulties drop it in the comment box below and I will get back to you ASAP.
This repo consists of a source code of a python script to detect plagiarism in textual document using cosine similarity
You might be wondering on how plagiarism detection on textual data is done, well it aint that complicated as you may think.
We all all know that computer are good at numbers, so in order to compute the simlilarity between on two text documents, the textual raw data is transformed into vectors => arrays of numbers and then from that we are going to use a basic knowledge vector to compute the the similarity between them.
This repo consist of a basic example on how to do that.
To get started with the code on this repo, you need to either clone or download this repo into your machine just as shown below;
git clone https://github.com/Kalebu/Plagiarism-checker-Python
