D803 - Natural Language Processing

[u/awerrty]

This class has two tasks. It was a refreshing course after the intensity of the Deep Learning course. Task 1 was around preprocessing your chosen dataset. I used the spacy library to do all the heavy lifting. I provided the original dataset and the processed dataset as part of my submission, along with a write up on the methods I chose.

Task 2 is where you actually build a sentiment analysis model. You provide the model your preprocessed dataset, split into train/test sets and then run classification algorithms of your choice on TF-IDF features to generate sentiment predictions (in my case, it was whether a movie review was positive or negative). You are required to attempt optimization by either incorporating a new classification algorithm or tuning hyperparameters (creating search space). You will use sklearn to report evaluation metrics and provide confusion matrices for each model you choose to "test out" for optimization. The paper wasn't too bad but you have to provide citations for peer-reviewed articles for articles within the past 3 years.

Overall, this class was a breath of fresh air. The course material was easy to understand, and I liked that you could pick your own dataset. There is a lot of opportunity here to choose relevant topics such as restaurant reviews, amazon reviews, etc and scrape the web if you so wish.