readme.md

Ch 7: Natural Language Processing

1. Introduction

• We introduce methods for turning text into numerical vectors. We introduce the TensorFlow 'embedding' feature as well.

2. Working with Bag-of-Words

• Here we use TensorFlow to do a one-hot-encoding of words called bag-of-words. We use this method and logistic regression to predict if a text message is spam or ham.

3. Implementing TF-IDF

• We implement Text Frequency - Inverse Document Frequency (TFIDF) with a combination of Sci-kit Learn and TensorFlow. We perform logistic regression on TFIDF vectors to improve on our spam/ham text-message predictions.

4. Working with Skip-Gram

• Our first implementation of Word2Vec called, "skip-gram" on a movie review database.

5. Working with CBOW

• Next, we implement a form of Word2Vec called, "CBOW" (Continuous Bag of Words) on a movie review database. We also introduce method to saving and loading word embeddings.

6. Implementing Word2Vec Example

• In this example, we use the prior saved CBOW word embeddings to improve on our TF-IDF logistic regression of movie review sentiment.

7. Performing Sentiment Analysis with Doc2Vec

• Here, we introduce a Doc2Vec method (concatenation of doc and word embeddings) to improve out logistic model of movie review sentiment.