Natural Language Processing

:dart: Learning Objectives

By the end of this lesson, you will be able to:

• Understand what Natural Language Processing (NLP) is and why it's important
• Identify real-world applications of NLP in your daily life
• Explain how sentiment analysis works
• Describe the steps to build an NLP model
• Apply basic NLP concepts to solve simple problems

:information_source: Natural Language Processing (NLP) is the process of helping computers understand, interpret, manipulate and potentially generate human language. It's one of the most important subfields in Artificial Intelligence.

Humans communicate in many languages like English, Chinese and Bahasa Malaysia. Computers only understand machine language or machine code.

Because of this difference, computers need to process our everyday language into something they can understand and respond to.

:memo: This interaction between humans and machines is what we call Natural Language Processing.

:iphone: Applications of NLP in Daily Life

NLP powers many tools you use every day:

• Language Translation - Apps like Google Translate help you understand different languages instantly

  

• Auto Correction - Word processors like Google Docs fix your spelling mistakes automatically

  

• Voice Assistants - Siri and Google Assistant understand your spoken commands

  tip Modern NLP doesn't just understand what you say - it also generates helpful responses! For example, you can activate Google Assistant by saying "Hey! Google"

:hammer_and_wrench: NLP Techniques

Now let's explore the main techniques that make NLP work in our daily lives.

Here are three major NLP techniques:

• Sentiment Analysis - Determines the emotion or opinion in text (positive, negative, or neutral)

• Language Translation - Converts text from one language to another while keeping the same meaning

• Keyword Extraction - Finds the most important words in text (like product codes or usernames)

:emoji: Sentiment Analysis

:information_source: Sentiment Analysis is the process of determining whether text expresses positive, negative or neutral emotions. It's one of the most popular NLP techniques!

The main goal is to identify and extract the meaning behind sentences. We call this meaning the sentiment.

Computers analyze the sentiment to understand its meaning and classify it accordingly.

Examples of Sentiment Analysis

Let's look at how sentiment analysis works with real examples:

Product Reviews:

• "This product is amazing!" -> Positive :emoji:
• "I guess the product is okay..." -> Neutral :emoji:
• "Your support team is useless" -> Negative :emoji:

Emotion Detection:

• "I like this flower a lot! Thank you!" -> Happy / Grateful :emoji:
• "I lost my wallet today..." -> Sad / Disappointed :emoji:

Real-World Applications

Sentiment analysis helps businesses and platforms every day:

• Customer Support - Companies analyze reviews and comments to understand how customers feel about their products

• Social Media Monitoring - Platforms like Facebook detect and remove harmful content automatically

:bulb: Want to try sentiment analysis yourself? Test it out with this free sentiment analysis tool from MonkeyLearn!

:wrench: Building an NLP Model for Sentiment Analysis

Sentiment analysis is a classification problem that we solve using machine learning.

The NLP model (a classifier) takes raw text as input and returns the category it belongs to.

Here's how to build an NLP machine learning model:

note These steps follow the same Machine Learning Process you learned earlier:

1. Data Preparation - Steps 1 and 2
2. Model Training - Steps 3 and 4
3. Model Testing - Step 5

Step One: Prepare Dataset

First, we collect data to train and test our ML model. The data needs raw text with labels so the model learns to recognize human language patterns.

Here's what the dataset looks like:

Raw Text	Labels
I managed to pass my test with flying colours!	Joy
I was scolded by my mom yesterday...	Sad
The test was easy.	Confident

Step 2: Text Processing

Next, we convert raw text into a format computers can understand (like numbers).

This happens in two phases:

• Text preprocessing - Cleaning and preparing the text
• Feature Extraction - Converting text to numerical values

Step 3: Choose the Correct Algorithm

Different algorithms produce models with different accuracy levels. Let's compare three classification algorithms from Chapter 7:

• K-Nearest Neighbor - Finds similar text examples
• Decision Tree Algorithm - Makes decisions based on text features
• Naive Bayes Algorithm - Uses probability to classify text

Step 4: Train the NLP Model

With our dataset and algorithm ready, we train the NLP model. The training process works just like training any other classification ML model.

Step 5: Test and Evaluate the Model

Finally, we test how accurately our model predicts sentiment. We use a testing dataset to get an accuracy score, then improve the model based on the results.

:books: Summary

In this lesson, you learned:

• NLP helps computers understand human language
• Common applications include translation apps, auto-correction, and voice assistants
• Sentiment Analysis identifies emotions in text (positive, negative, or neutral)
• Building an NLP model follows 5 steps: prepare data, process text, choose algorithm, train model, and test results
• Different algorithms (K-NN, Decision Tree, Naive Bayes) work better for different NLP tasks

Video

:emoji: AI Practice Prompts

Ready to explore NLP with AI? Try these prompts:

Understanding NLP:

• "Explain how computers understand human language in simple terms"
• "What are 5 everyday examples of NLP in action?"

Hands-on Coding:

• "Write Python code to analyze if a movie review is positive or negative"
• "Show me how to extract keywords from a paragraph of text"

Real-world Applications:

• "How does Google Translate work behind the scenes?"
• "Create a simple chatbot that can understand basic questions"

:bulb: Tip Start with the understanding prompts first, then move to coding once you grasp the concepts!