You can select your own set of entities for a specific practical task, process the texts with this set, and train the model. This scenario is ubiquitous and makes NER one of the most frequently solved NLP problems in the industry.


Intro to NLP Free ITIL 4 books
Intro to NLP

Introduction to NLP with sentiment analysis of text data

People communicate in any language and use text or words. Now, in order for computers to interact with people, they must understand the natural language that people speak. Natural language processing teaches computers to understand, process and use natural language.

In this article, we will look at some of the common techniques used in NLP problems. And let's create a simple sentiment analysis model using movie reviews to predict positive or negative ratings.

What is Natural Language Processing (NLP)?

NLP is one of the branches of artificial intelligence that works with the analysis, understanding and generation of living languages ​​in order to interact with computers both orally and in writing, using natural languages ​​instead of computer ones.

Data Cleanup

While cleaning data, we remove special characters, symbols, punctuation marks, html tags, etc. from the original data, which does not contain any useful information for the model, and we just add noise to the data.

What to remove from the source data and what not to depend on the problem statement. For example, if you're working with text from the economics or business field, signs like $ or other currency symbols may contain hidden information that you don't want to miss. But in most cases, we eliminate them.

Pretreatment of data

Data preprocessing (doctranslator as an example) is a data mining step that involves converting the raw data into an understandable format.