Natural Language Processing Projects using Python

Natural Language Processing (NLP) is the field of Artificial Intelligence which means to train a computer system or any machine to understand human languages. NLTK and spaCy are two of the most used Python packages for the tasks of NLP. In this article, I will introduce you to 3 amazing Natural Language Processing Projects using Python.

Natural Language Processing Projects using Python

NLP is mostly used in online services based companies to understand what a customer needs and how a customer reacts. For example, Amazon now enables you to search in more languages than English which simply improves user experience on their platform. To create am amazing project based on NLP you have to think a lot, but to save some of your time below are some of the amazing projects based on Natural Language Processing and explained using Python.

Real-Time Sentiment Analysis:

Sentiment Analysis is an application of natural language processing that is used to understand people’s opinions. Today, many companies use real-time sentiment analysis by asking users about their service. 

The main purpose of sentiment analysis is to analyze the opinions of users of a particular product or service, which helps customers understand the quality of the product. For example, every time Apple releases a new iPhone, we see a lot of people giving their opinion on it, some like it and some criticize it, in the end, all people’s opinions help us decide whether we should buy the new iPhone or not.

Text Emotions Detection:

A human can express his emotions in any form, such as face, gestures, speech and text. The detection of text emotions is a content-based classification problem. In machine learning, the detection of textual emotions is the problem of content-based classification, which is the task of natural language processing.

Detecting a person’s emotions is a difficult task, but detecting the emotions using text written by a person is even more difficult as a human can express his emotions in any form. Usually, emotions are expressed as joy, sadness, anger, surprise, hate, fear, etc. Recognizing this type of emotion from a text written by a person plays an important role in applications such as chatbots, customer support forum, customer reviews etc.

Facebook Posts Sentiment Analysis:

Facebook is a very good platform to perform sentiment analysis task because users are free to express their opinions on any topic be it political or environmental, users are free to share their opinions.

For the Facebook posts sentiment analysis task, you need to extract your data from Facebook first, which is a very easy task, just follow the steps mentioned below:

  1. Go to settings & privacy
  2. Then go to settings
  3. From the left click on Your Facebook Information
  4. Click on view at Download your information
  5. Then only select posts and click on create file.

Facebook will send you a notification in the next 60 minutes to download your data. You have to search for “your_posts_1.json” file in the downloaded data, as we only need this data fro the task of Facebook posts sentiment analysis with Python.


In this article, I introduced you to some of the amazing Natural Language Processing projects solved and explained using Python. These projects will help you to work on the practical use case on NLP. I hope you liked this article on Natural Language Processing projects. Feel free to ask your valuable questions in the comments section below.

Aman Kharwal
Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of data📈.

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