This repository contains code and associated files for deploying a Sentiment Analysis Web App using AWS SageMaker. This project is part of Udacity's Machine Learning Nanodegree.
In this project, the task is to build a plagiarism detector that examines a text file and performs binary classification; labeling that file as either plagiarized or not, depending on how similar that text file is to a provided source text. Detecting plagiarism is an active area of research; the task is non-trivial and the differences between paraphrased answers and original work are often not so obvious.
This project will be broken down into three main notebooks:
Notebook 1: Data Exploration
- Load in the corpus of plagiarism text data.
- Explore the existing data features and the data distribution.
Notebook 2: Feature Engineering
- Clean and pre-process the text data.
- Define features for comparing the similarity of an answer text and a source text, and extract similarity features.
- Select "good" features, by analyzing the correlations between different features.
- Create train/test
.csvfiles that hold the relevant features and class labels for train/test data points.
Notebook 3: Train and Deploy Your Model in SageMaker
- Upload train/test feature data to S3.
- Define a binary classification model and a training script.
- Train the model and deploy it using SageMaker.
- Evaluate the deployed classifier.
Please see the README in the root directory for instructions on setting up a SageMaker notebook and downloading the project files (as well as the other notebooks).
Please see the README in the root directory for instructions on setting up a SageMaker notebook and downloading the project files (as well as the other notebooks).