End To End Guide For NLP Project

Explains How To Build A Successful NLP Project

NLP projects are gaining popularity due to the vast amount of textual data that is readily available and is being created on daily basis. This article demonstrates how we can implement a NLP project.

Often we don’t take advantage of this data

The textual data can be gathered from the feedbacks users have given on products, tweets of observers, social media statuses and comments, news articles, emails, SMSs, chat rooms, information on web pages, video channels and so on.

We can use all of the data to gain better insights

Article Aim

This article serves the role of a guide that explains the steps that are required to implement a NLP project. It can be overwhelming to read an article that includes everything that we need to know in a single article. Hence, I have written a number of easy to understand articles on NLP project and this article presents them together in their logical order. Consequently, it enables readers to build an excellent NLP project from the start to the end.

This article will provide a thorough understanding of the NLP topics.

Stage 1. Build The Foundation

The steps mentioned in the first stage is all about building a strong foundation of the subject.

A1. Understanding NLP And Sentiment Analysis

Let’s understand what NLP is and what we are planning to implement.

A2. Programming Familiarity

We need to get familiar with a programming language. I recommend Python. This article covers everything that we need to know about Python.

A3. Python 3.8 Features

Python latest version is going to be 3.8. It is recommended to Review The New Python Features so that we don’t reinvent the wheel in our project

A4. Useful Data Manipulation Libraries

There are two libraries which we rely heavily on for data manipulation: Pandas And Numpy.

Pandas

Numpy

A5. Application Design

NLP project needs to be designed in a componentised manner. Ensure The Design Of The Application Is Robust As It Can Save You Time And Effort In The Future

Stage 2. Let’s Start The Real Work

Now that our foundation is solid, this stage is all about providing an understanding on the actual steps we need to perform to build a NLP project.

B1. Data Extraction

The first key task is to find where the data is and how to extract the data. This article demonstrates how we can use Python For Data Extraction From Social Media, Emails, Documents, Webpages, RSS & Images

B2. Data Processing

Once we have gathered the text for our project, we might encounter that the data is unstructured. The next key task is to process the text so that it is clean, concise and meaningful. Therefore, we need to perform Text Processing In Data Science Projects

B3. Tagging, Stemming and Lemmatisation

The next step is to tag the words via Text Part Of Speech Tagging. Additionally, we are ready to perform Stemming And Lemmatisation In The NLP Data Science Project.

Part Of Speech Tagging

Stemming And Lemmatisation

B4. Text Mining Algorithms

The text needs to be converted to numbers. There are multiple algorithms that compute and use the frequency of the words or group them to help us understand their hidden meanings.

B5. Word2Vec Algorithm

Now the numerical data needs to be fed into a model so that we can start forecasting it. We can feed our data to a model. Word2Vec algorithm is gaining populatity. Let’s understand how to Predict Text Using Word2Vec Model

B6. Neural networks

Neural networks are great for understanding and building data with non linear relationships. This section encourages readers to Explore Neural Networks And Deep Learning So That They Can Build Sophisticated Models.

B6. 1: Overview Of Neural Networks

B6. 2 Understand Neural Networks Activation Types

B6. 3 Understand What Weights And Biases Are

B6. 4 Get Familiar With What Neural Network Neurons Are

B6. 5 Ensure You Understand What Hidden Layers Are

B6. 6 Potentially Have a Look At Neural Network Usecases So You Can Get A Stronger Grip On The Concepts

B7. Evaluate NLP Model

Now that the NLP algorithm has started to forecast text, the last step is about assessing the accuracy of the model. Learn How To Evaluate The Model Performance

Summary

NLP projects are gaining popularity due to the vast amount of textual data that is readily available and is being created on daily basis.

This article served as a guide and presented an overview of how we build a successful NLP project.

Hope it helps