NLP Intro for Text - Sentiment Analysis with Deep Learning

Introduction to Natural Language Processing (NLP)

Natural Language Processing, otherwise known simply as NLP, is a vast subfield of linguistics, computer science, and artificial intelligence that is focused on human-machine interaction.

Specifically, this field focuses on the use of computers to process and analyze human language data. Generally speaking, human languages are often referred to as natural language in this field.

There are multiple NLP tasks that are active and in use today, with many more continuing to be developed and improved upon. Some common NLP tasks include:

• Text classification
• Speech recognition
• Automated chat bots
• Optical character recognition (OCR)
• Text-to-speech
• Machine translation
• Automatic summarization

As we can see from these few examples, the data in this field can be comprised of text, audio, video, and images, as natural language can be captured in each of these data types.

In addition to the different data types in this field, there are also many different algorithms used in this field to tackle different tasks. Some algorithms are based on a rule-based approach, while some use machine learning, and others use artificial neural networks.

Given the vast differences between tasks, algorithms, and data types in this field, the way that we work with and process the data to complete these tasks varies greatly between them.

In this course, the type of data we'll be working with is textual data, the type of algorithms that we'll be using to process this data is artificial neural networks, and the particular types of tasks we'll be focusing on fall under text classification.

Text classification has a broad array of use cases, with some current popular ones being:

• Sentiment analysis
• Spam email detection
• Document organization
• Inappropriate comment flagging

We've now been generally introduced to the field of NLP and have an understanding of the subset of the field for which our focus will lie in this course.