Course Description
Step-by-step programming tutorials where you learn how to implement text classifiers in PHP.
In this course you will learn how to:
1- Extract features from text, continuous, and discrete features.
2- Build a feature extractor from text using PHP.
3- Build a Naive Bayes text classifier and apply it to Dialog Act classification.
4- Build a k-NN-based text classifier and apply it to text sentiment analysis.
You will also learn:
5- The source code in PHP for the classification systems that are taught in the course.
6- A trained model that could be used immediately for Dialog Act classification (very useful when building BOTs) and sentiment analysis.
What am I going to get from this course?
- Implement a text classifier using a programming language of your choice.
- Access the sourcecode and trained models for a Dialog Act classifier and Sentiment Analysis system (very useful for building BOTs and analyzing user comments)
Curriculum
Module 1: The Basics
46:26
This is an introduction that summarizes the content of the course
Lecture 2
Discrete Word Features
08:55
The first step in classification is feature extraction. This video presents the extraction of discrete features from text.
Lecture 3
Continuous Word Features
04:23
This videos presents Word Embeddings or Continuous features.
Lecture 4
Text Classification using Naive Bayes
11:27
The principle of Naive Bayes classifiers is introduced
Lecture 5
Naive Bayes Example
18:32
A simulation example of text classification using Naive Bayes and Discrete Features
Module 2: Dialog Act Classification
50:23
Lecture 6
Data Preparation
05:19
Preparing the data that will be used to train the Dialog Act Classifier.
Lecture 7
Dialog Act Feature Extraction
14:08
Implement a discrete feature extractor adapted for Dialog Act classification.
Lecture 8
Dialog Act Feature Extraction (Run the script)
04:42
Testing feature extraction on some examples.
Lecture 9
Naive Bayes Classifier Training - 1
11:22
Implement the training function for a Naive Bayes Classifier.
Lecture 10
Naive Bayes Classifier Training - 2
05:42
Test the Naive Bayes training function that was implemented in the previous Lecture.
Lecture 11
Naive Bayes Classifier Testing
09:10
Implement the classification function.
Quiz 1
Save then load a trained model on disk
As you saw in the previous lectures, we can train a Naive Bayes Model to do text classification.
a- Use the attached training data to train a Dialog Act Classifier.
b- Save the trained model on a file on disk.
c- Update the script so that you can load an already trained model to do classification instead of training a model each time.
Module 3: Sentiment Analysis
39:18
Lecture 12
Sentiment Analysis Using Continuous Word Features
11:38
Use continuous word features associated with KNN classifier for sentiment analysis
Lecture 13
Build a K-NN Based Sentiment Classifier - 1
15:37
Use word embeddings (continuous features) and K-NN to build a sentiment classifier of words.
Lecture 14
Training Data
00:58
A description of the training data that could be used for sentiment analysis.
Lecture 15
Build a K-NN Based Sentiment Classifier - 2
02:49
Try the K-NN Sentiment Classifier that was built in the previous video.
Lecture 16
Use the Dot Product to Build a Sentiment Classifier
05:39
Use the centroid of word embeddings and the dot product to build a classifier.
Lecture 17
How to Improve the Sentiment Classifier
02:37
Some ideas on how one can make the sentiment classifier adaptable to sentences are presented in this video.
