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Plagiarism Detection and Subreddit Classification

Overview

This repository contains the solution to a set of tasks for detecting plagiarism in sentences and classifying subreddit comments. The implementation is done in Python 3.8+ and utilizes word2vec vectors pre-trained on Google News, logistic regression, and various Python libraries such as gensim, json, sklearn, scipy, and numpy.

Repository Contents

• plagiarism_and_subredditClassification.py: Contains the main functions for the tasks.
• plagiarism_and_subredditClassification_grader.py: Grader script to test the implementation.
• redditComments_train.jsonlist: Training data for the subreddit classification task.
• redditComments_test_notGraded.jsonlist: Test data for evaluating the subreddit classification model.

Tasks

Task 1: Plagiarism Detection

Function: findPlagiarism(sentences, target)

This function identifies which sentence in a list of sentences was most likely plagiarized to create a target sentence. It leverages word2vec vectors to compare the semantic similarity between sentences.

Input:

• sentences: A list of strings, where each string is a sentence.
• target: A string representing a sentence that was plagiarized by rewriting words/phrases to those that are similar.

Output:

• Returns the index of the sentence in sentences which is most likely to have been used to write target.

Function: setModel(model)

This function is used to set the global word2vec model. It is called once at the beginning of the script to save the model as a global variable and perform any necessary preprocessing.

Task 2: Subreddit Classification

Function: classifySubreddit_train(trainFile)

This function trains a logistic regression model to classify comments into subreddits using pre-trained word2vec vectors.

Input:

• trainFile: The name of a jsonlist file where each line is a JSON object containing a comment and the corresponding subreddit.

Output:

• Trains and stores the logistic regression model for subreddit classification.

Function: classifySubreddit_test(comment)

This function uses the trained logistic regression model to classify a single comment into a subreddit.

Input:

• comment: A string representing the raw text of a single comment.

Output:

• Returns a string which is the name of the subreddit the comment is predicted to belong to.

Requirements

• Python 3.8+
• gensim
• json
• sklearn
• scipy
• numpy

Setup

1. Ensure Python 3.8+ is installed on your machine.
2. Install the required libraries using pip:

   pip install gensim json sklearn scipy numpy

3. Place the GoogleNews-vectors-negative300.bin file in the same directory as plagiarism_and_subredditClassification.py.

Usage

1. Set the word2vec model by calling setModel(model) at the beginning of your script.
2. Use findPlagiarism(sentences, target) to detect plagiarism.
3. Train the subreddit classifier using classifySubreddit_train(trainFile).
4. Classify comments using classifySubreddit_test(comment).