Natural language processing (NLP) is a branch of artificial intelligence designed to process and analyze language-based data to improve communication between humans and computers. This technology allows computers to hear speech and read text and execute meaningful tasks accordingly. NLP is the driving force behind speech-recognition applications and online translation software.
Because of this, tech companies are always looking for professionals with NLP skills. Glassdoor estimates that natural language processing (NLP) engineers earn about $120,743 per year, which is more than the national average for all occupations.
If you want to start a career in this field, you should consider getting as much experience as possible by completing NLP projects that call out to you. Projects are a great way to build your portfolio and increase your marketability. This article covers some of the best NLP projects to help you sharpen your skills.
5 Skills That NLP Projects Can Help You Practice
Learning natural language processing is easier through projects. NLP projects are an efficient way of honing your skills as you learn through practice and repetition. Here are some key skills that NLP projects can help you improve.
- Mathematics. A solid foundation of mathematics and statistics can help you understand NLP. Familiarity with linear algebra, calculus, probability theory, and statistics is key in NLP, and you’ll be learning advanced concepts in these fields as you work on some of the projects below.
- Programming. NLP uses a wide variety of programming languages including Java, R, and Python. You need to master these programming languages to implement any NLP application. The best way to enhance your coding skills in these programming languages is through real-world projects.
- Data Structures. A solid foundation in data structures, especially trees, hash tables, and heaps is important in NLP development. NLP projects can help you practice your data structure skills, which will also come in handy for other machine learning projects.
- Algorithms. One way to develop your knowledge of algorithms is by practicing with NLP projects. NLP algorithms can be used to create meaningful summaries of books, news articles, blog posts, and other materials.
- Front End and User Interface Technology. UI design skills are useful for presenting visualizations of neural networks to people who might not have advanced knowledge. Taking advantage of front end frameworks like Flask and Django, you can build intuitive interfaces that make your NLP applications even better.
Best NLP Project Ideas for Beginners
You should consider easy and simple NLP projects when starting out. These projects will help you lay a solid foundation that will be useful for learning advanced concepts. Here are some of the best beginner NLP projects.
Chatbots
- NLP Skills Practiced: Python, decision trees, NLTK library, Bag-of-words model, Naive Bayes classifier
Chatbots are becoming increasingly popular on the Internet. They interact with customers to provide answers to questions in an efficient manner. In this project, you will hone your Python skills, particularly text classification, text preprocessing, tokenization, Parts-of-Speech tagging, and lemmatization, by building a complete chatbox application.
Summary Writer App
- NLP Skills Practiced: NLP algorithms
People prefer reading news stories and articles that are short. A summary writer app shortens long news articles using text summarization algorithms. This project will give you a better understanding of text summarization. You will gain insights into how NLP algorithms work when ranking sentences based on their significance or relevance.
Sentiment Analysis System
- NLP Skills Practiced: Python, R, RESTful API
This project involves the use of NLP algorithms to identify and categorize opinions expressed in a given text. This is done to determine if the writer’s tone is positive, negative, or neutral. To work on this project, you’ll need to have a solid foundation in R or Python, as well as knowledge of the Naive Bayes classifier and RESTful API.
Predictive Text Generator
- NLP Skills Practiced: NLP algorithms, machine translation, fastText model
Predictive text generators are now very common. They are applications that use computational models to analyze the probability of two or more words appearing together in a text, thus helping people write messages and emails faster. In this project, you’ll put your skills to the test by developing a model that generates chains of words that are probably related.
Language Identifier
- NLP Skills Practiced: fastText model
Search engines like Google use sophisticated neural network models to detect the language in which a webpage is written. As an NLP beginner, you can create your own language identifier by using the fastText model created by Facebook.
Best Intermediate NLP Project Ideas
Here are some of the best intermediate NLP project ideas to consider to push yourself to the next level. Most of these projects will prepare you for advanced NLP projects.
Spelling Correction Model
- NLP Skills Practiced: Python, TextBlob
TextBlob is a Python library used to process text data. It provides a simple API for natural language processing tasks and can be used for classifying and analyzing feelings. In this project, the author of the post shows you how to use TextBlob and the Python programming language to write a program that corrects spelling errors.
Autocorrect App
- NLP Skills Practiced: Python, NLP libraries, textdistance
Almost all smartphone manufacturers incorporate autocorrect features into their smartphones. Autocorrect is a popular technology based on machine learning and, more specifically, on NLP. In this project, you will build an autocorrect application from scratch using Python and a library known as textdistance.
Whatsapp Group Analysis
- NLP Skills Practiced: Python, NLP libraries
This project entails analyzing data from a WhatsApp chat group using several libraries, including NumPy, Seaborn, Plotly, and Pandas. First, you will extract the messages from the app and then format them using your Python skills so that you can analyze them. The author also teaches you how to create a word cloud to see the most commonly used words.
Fake News Detection Model
- NLP Skills Practiced: Python, NLP libraries
In this project, you’ll use basic data science libraries such as NumPy, Pandas, and Transformers to create a machine learning model capable of detecting fake news and misinformation. This project involves tokenization, vectorization, and modeling. You’ll be using a large, open-source CSV dataset.
Predicting US Elections
- NLP Skills Practiced: Python, database management, NLP libraries
The US election is a key area of interest for machine learning engineers. It is possible to create a model that can predict the results of an election by performing sentiment analysis on social media apps like Twitter. In this project, you’ll work with Pandas, NumPy, and TextBlob to develop a model that performs sentiment analysis using datasets collected from Twitter.
Advanced NLP Project Ideas
Advanced NLP projects allow you to test your skills by tackling complex NLP problems. These projects are designed for expert or experienced neural network engineers and developers. Here are some of the best advanced NLP project ideas.
Toxic Comment Classification
- NLP Skills Practiced: Python, NLP algorithms
This is an advanced project that will push your NLP skills to the limit. You will develop deep learning models that classify social media comments into different categories, the end goal being to eliminate hurtful or toxic comments from these platforms.
Speech Recognition
- NLP Skills Practiced: Database management, full stack development, NLP algorithms
Automatic speech recognition (ASR) involves using NLP algorithms to identify and analyze human voice commands. This is a very important technology with applications in many fields, including education, cyber security, and search engines. Building your own ASR model will take time and effort but it will help you develop in-demand skills and boost your employability.
Neural Machine Translator
- NLP Skills Practiced: Tokenization, database management, neural networks
This project uses neural machine translation, a recent variation of machine translation that mimics how the human brain functions. Building a neural machine translator from scratch will be a challenging project for even the most experienced engineers. You will need knowledge of preprocessing, word embeddings, and sequence to sequence encoding and decoding.
Speech Emotion Analyzer
- NLP Skills Practiced: Jupyter Notebook, Keras, deep neural networks, deep learning
The goal of this project is to create a neural network model capable of detecting emotions from everyday conversations. You will build a model that can detect up to five different emotions from human language.
The applications of such a model are manifold, from adjusting the speed of cars to avoid collisions based on a person’s mood to helping marketers recommend products that match the consumer’s emotional state.
Paraphrase Detection
- NLP Skills Practiced: HTML, Python, deep learning
Paraphrase detection is a common NLP technique used to determine whether two sentences have the same meaning. In order to achieve a high level of accuracy, the sentences need to be syntactically and semantically analyzed. This is an academically challenging NLP problem that has been featured in the Kaggle Quora Question Pairs competition.
NLP Starter Project Templates
Starter templates help developers and programmers save time by giving them access to prewritten code and structure. These templates can be very helpful for beginners that need guidance to complete a project. Here are some NLP starter projects to consider.
- Tensorflow Project Template Architecture. This template is designed for any deep learning project, especially in NLP and TensorFlow. It is easy to use and will save you tons of time.
- Python Project Template. You can use this simple and intuitive template for starting multiple types of applications.
- PyTorch Natural Language Processing Project Template. This repository is great for computer vision tasks. It provides a low-level project structure to make the most of whichever framework you choose to employ.
Next Steps: Start Organizing Your NLP Portfolio
The best way to showcase your skills in the tech industry is by highlighting your best creations in a portfolio. Developers and programmers use portfolios to show employers what they are capable of. Here are a few tips for organizing your portfolio.
Focus on Readability and Clarity
Portfolios should be easy to read and navigate. Whether you are looking for a job in front end or backend development, your portfolio should be concise and clear. Be sure to include project descriptions that explain the project, your process, and the issues you encountered.
Show Your Full Range of Skills
Include projects that touch on different skills and topics to give employers an idea of the extent of your knowledge. Having variety in your portfolio shows that your expertise spans a wide spectrum of topics.
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Include Links to GitHub Repositories
GitHub is a popular platform that lets you work from anywhere and collaborate with other developers. Creating a GitHub repository for your projects and including the links in your portfolio will make your work accessible to any employer interested in hiring you.
NLP Projects FAQ
An NLP project consists of several phases. Oftentimes, you’ll start with data collection and reprocessing. This is followed by model development, feature extraction, assessing language models, and deployment.
Yes, NLP projects are very helpful if you are looking to learn about machine learning and artificial intelligence. Through practice and repetition, you will develop problem-solving and technical skills.
Common applications of natural language processing include chatbots and virtual assistants. Other real-world uses of NLP are automatic text summarization, sentiment analysis, text labeling, and neural machine translation.
Some popular NLP projects include creating a chatbot and a neural machine translator, predicting the results of elections based on sentiment analysis on social media, and building a speech recognition system.
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