20 NLP Projects with Source Code for NLP Mastery in 2024

Natural Language Processing (NLP) is an interdisciplinary field that focuses on the interactions between humans and computers using natural language. With the rise of digital communication, NLP has become an integral part of modern technology, enabling machines to understand, interpret, and generate human language. This blog explores a diverse list of interesting NLP projects ideas, from simple NLP projects for beginners to advanced NLP projects for professionals that will help master NLP skills. 

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According to a report by the US Bureau of Labor Statistics, the jobs for computer and information research scientists are expected to grow 22 percent from 2020 to 2030. As per the Future of Jobs Report released by the World Economic Forum in October 2020, humans and machines will be spending an equal amount of time on current tasks in the companies, by 2025. The report has also revealed that about 40% of the employees will be required to reskill and 94% of the business leaders expect the workers to invest in learning new skills. They are showing great interest in adopting cloud computing along with other technologies like non-human robots, artificial intelligence (AI), and encryption.

All the numbers presented above suggest that there will be a huge demand for people who are skilled at implementing AI-based technologies. One such sub-domain of AI that is gradually making its mark in the tech world is Natural Language Processing (NLP). You can easily appreciate this fact if you start recalling that the number of websites or mobile apps, you’re visiting every day, are using NLP-based bots to offer customer support.

As we already revealed in our Machine Learning NLP Interview Questions with Answers in 2021 blog, a quick search on LinkedIn shows about 20,000+ results for NLP-related jobs. Thus, now is a good time to dive into the world of NLP and if you want to know what skills are required for an NLP engineer, check out the list that we have prepared below.

Skills Required to Become An NLP Engineer

• Comfortable with implementing NLP techniques in at least one of the popular deep learning frameworks (PyTorch, Tensorflow, etc.).

• Good knowledge of commonly used machine learning and deep learning algorithms.

• Strong understanding of statistical techniques used to quantify the results of NLP algorithms.

• Hands-on experience with cloud-based platforms such AWS, Azure.

• Past experience with utilizing NLP algorithms is considered an added advantage.

• Utilize natural language data to draw insightful conclusions that can lead to business growth.

• Design NLP-based applications to solve customer needs.

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20+ NLP Projects Ideas to Practice

Apart from the skills mentioned above, recruiters often ask applicants to showcase their Project portfolios. They do so in order to have an idea of how good you are at implementing NLP algorithms and how well you can scale them up for their business. To help you in overcoming this challenge, we have prepared an informative list of Natural Language Processing Projects. And to make your browsing hassle-free, we have divided the projects into the following four categories:

1. Interesting NLP Projects for Beginners

2. Simple NLP Projects

3. Advanced NLP Projects

4. GitHub NLP Projects

5. NLP Open-source Projects

So, go ahead, pick your category and try implementing your favorite projects today!

Interesting NLP Projects for Beginners

In this section of our NLP Projects blog, you will find NLP-based projects that are beginner-friendly. If you are new to NLP, then these NLP full projects for beginners will give you a fair idea of how real-life NLP projects are designed and implemented.

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NLP Projects Idea #1 Sentiment Analysis

This is one of the most popular NLP projects that you will find in the bucket of almost every NLP Research Engineer. The reason for its popularity is that it is widely used by companies to monitor the review of their product through customer feedback. If the review is mostly positive, the companies get an idea that they are on the right track. And, if the sentiment of the reviews concluded using this NLP Project are mostly negative then, the company can take steps to improve their product.

Method: The first step to start designing the Sentiment Analysis system would involve performing EDA over textual data. After that, you will have to use text data processing methods to extract relevant information from the data and remove gibberish. The next step would be to use significant words in the reviews to analyze the sentiment of the reviewer. Through this project, you can learn about the TF-IDF method, Markov Chain concept, and feature engineering. If you want a detailed solution for this project in python programming language, check out this project from our repository: Ecommerce product reviews - Pairwise ranking and sentiment analysis.

Recommended Reading: How to do text classification?

NLP Projects Idea #2 Conversational Bots: ChatBots

As we mentioned at the beginning of this blog, most tech companies are now utilizing conversational bots, called Chatbots to interact with their customers and resolve their issues. This is a very good way of saving time for both customers and companies. The users are guided to first enter all the details that the bots ask for and only if there is a need for human intervention, the customers are connected with a customer care executive.

Method: In this project, you will learn how to use the NLTK library in Python for text classification and text preprocessing. You will also get to explore how Tokenization, lemmatization, and Parts-of-Speech tagging are implemented in Python. Through this project, you will get accustomed to models like Bag-of-words, Decision tree, and Naive Bayes. To look at a more detailed solution to the solution of this project, check out the chatbot example application using python - text classification using nltk.

NLP Projects Idea #3 Topic Identification

This is a very basic NLP Project which expects you to use NLP algorithms to understand them in depth. The task is to have a document and use relevant algorithms to label the document with an appropriate topic. A good application of this NLP project in the real world is using this NLP project to label customer reviews. The companies can then use the topics of the customer reviews to understand where the improvements should be done on priority.

Method: This project will introduce you to methods of handling textual data and using regex You will understand how to convert textual data into vectors through methods like TF-IDF and Count vectorizer. You will also learn how to use unsupervised machine learning algorithms for grouping similar reviews together. To know more, read Topic Modeling using K Means Clustering.

Recommended Reading: K-means Clustering Tutorial-Machine Learning

NLP Projects Idea #4 Automatic Text Summarization 

We are all living in a fast-paced world where everything is served right after a click of a button. People now want everything to be given to them at a fast speed. And that is why short news articles are becoming more popular than long news articles. One such instance of this is the popularity of the Inshorts mobile application that summarizes the lengthy news articles into just 60 words. And the app is able to achieve this by using NLP algorithms for text summarization.

Method: This one of the top NLP project ideas that will help you in understanding how to use NLP algorithms for ranking various sentences in the document based on their significance. You will have to use algorithms like Cosine Similarity to understand which sentences in the given document are more relevant and will form the part of the summary.

NLP Projects Idea #5 Grammar Autocorrector

Gone are the days when one will have to use Microsoft Word for grammar check. Nowadays, most text editors offer the option of Grammar Auto Correction. There is even a website called Grammarly that is gradually becoming popular among writers. The website offers not only the option to correct the grammar mistakes of the given text but also suggests how sentences in it can be made more appealing and engaging. All this has become possible thanks to the AI subdomain, Natural Language Processing.

Method: This NLP project will require you to not use advanced machine learning algorithms. You should train your algorithms with a large dataset of texts that are widely appreciated for the use of correct grammar. For training, it’s a must that you perform necessary NLP techniques like Lemmatization, Removal of stop words/ irrelevant words, Removal of punctuations, etc.

NLP Projects Idea #6 Spam Classification

Recall those not-so-good old days of using emails where we used to receive so many junk emails and very few relevant emails. We have come so far from those days, haven’t we? A good amount of credit for this transformation goes to NLP. Using the NLP algorithms, email service providing systems can identify spam emails easily which helps their user base in saving time by avoiding unnecessary emails in their inbox.

Method: For this NLP project, you will have to collect a dataset of emails and then use the body of the email for training your algorithm. You can use deep learning or machine algorithms to achieve this but as a beginner, we’d suggest you stick to machine learning algorithms as they are relatively easy to understand. 

NLP Projects Idea #7 Text Processing and Classification

For newbies in machine learning, understanding Natural Language Processing (NLP) can be quite difficult. To smoothly understand NLP, one must try out simple projects first and gradually raise the bar of difficulty. So, if you are a beginner who is on the lookout for a simple and beginner-friendly NLP project, we recommend you start with this one.

Project Objective: Understand NLP from scratch by working on the simple problem of text classification.

Learnings from the Project: Your first takeaway from this project will be data visualization and data preprocessing. Additionally, you will learn about Stopwords, Tokenisation, Stemming using Lancaster Stemmer, N-grams model, TF-IDF. You will also get to explore the implementation of the logistic regression model on a textual dataset.

Tech Stack: Language: Python, Libraries:  pandas, seaborn, matplotlib, sklearn, nltk

Access the full solution to NLP Project for Beginners on Text Processing and Classification 

Simple NLP Projects

This heading has those sample  projects on NLP that are not as effortless as the ones mentioned in the previous section. For beginners in NLP who are looking for a challenging task to test their skills, these cool NLP projects will be a good starting point. Also, you can use these NLP project ideas for your graduate class NLP projects.

NLP Projects Idea #1 Sentence Autocomplete

This is an exciting NLP project that you can add to your NLP Projects portfolio for you would have observed its applications almost every day. Wondering where? Well, it’s simple, when you’re typing messages on a chatting application like WhatsApp. We all find those suggestions that allow us to complete our sentences effortlessly. Turns out, it isn’t that difficult to make your own Sentence Autocomplete application using NLP.

Method: This is the perfect NLP project for understanding the n-gram model and its implementation in Python. You can use various deep learning algorithms like RNNs, LSTM, Bi LSTMs, Encoder-and-decoder for the implementation of this project. Of course, you will first have to use basic NLP methods to make your data suitable for the above algorithms.

NLP Projects Idea #2 Market Basket Analysis

Every time you go out shopping for groceries in a supermarket, you must have noticed a shelf containing chocolates, candies, etc. are placed near the billing counter. It is a very smart and calculated decision by the supermarkets to place that shelf there. Most people resist buying a lot of unnecessary items when they enter the supermarket but the willpower eventually decays as they reach the billing counter. Another reason for the placement of the chocolates can be that people have to wait at the billing counter, thus, they are somewhat forced to look at candies and be lured into buying them. It is thus important for stores to analyze the products their customers purchased/customers’ baskets to know how they can generate more profit.

Method: This NLP project will give you a great idea about how Market Basket Analysis is relevant for companies. You will understand different association rules and learn the apriori and the Fp Growth algorithm. You will also get to know about univariate and bivariate analysis. To know more about this NLP project, refer to Market basket analysis using apriori and fpgrowth algorithm tutorial example implementation.

NLP Projects Idea #3 Automatic Questions Tagging System

Sites that are specifically designed to have questions and answers for their users like Quora and Stackoverflow often request their users to submit five words along with the question so that they can be categorized easily.  But, sometimes users provide wrong tags which makes it difficult for other users to navigate through. Thus, they require an automatic question tagging system that can automatically identify correct and relevant tags for a question submitted by the user.

Method: For implementing this project you can use the dataset StackSample. It is a huge dataset that has three files: Answers, Questions, and Tags. All three files are in CSV format so you can use the Python Pandas library to perform the necessary analysis. The three files are connected by the column ‘id’ which is unique for each question. Each question has at least three tags and your task is to predict these tags using questions and answers.

NLP Projects Idea #4 Resume Parsing System

A resume parsing system is an application that takes resumes of the candidates of a company as input and attempts to categorize them after going through the text in it thoroughly. This application, if implemented correctly, can save HR and their companies a lot of their precious time which they can use for something more productive.

Method: This parsing system can be built using NLP techniques and a generic machine learning framework. Through this NLP project, you will understand Optical Character Recognition and conversion of JSON to Spacy format. As resumes are mostly submitted in PDF format, you will get to learn how text is extracted from PDFs.  Access the source code for Resume Parsing, refer to Implementing a resume parsing application.

NLP Projects Idea #5 Disease Diagnosis

If you are looking for NLP in healthcare projects, then this project is a must try. Natural Language Processing (NLP) can be used for diagnosing diseases by analyzing the symptoms and medical history of patients expressed in natural language text. NLP techniques can help in identifying the most relevant symptoms and their severity, as well as potential risk factors and comorbidities that might be indicative of certain diseases.

Method: NLP techniques can be used to extract information from unstructured clinical notes and electronic health records, which can be used to predict and diagnose diseases. This information includes patient demographics, medical history, medication and treatment plans, and laboratory results. You can use NLP to identify specific patterns or signals within the text data that might be indicative of a particular disease or condition.

Advanced NLP Projects

If you consider yourself an NLP specialist, then the projects below are perfect for you. They are challenging and equally interesting projects that will allow you to further develop your NLP skills.

NLP Projects Idea #1 Language Recognition

How often have you traveled to a city where you were excited to know what languages they speak? That’s such a common thing. To discover a language, you don’t always have to travel to that city, you might even come across a document while browsing through websites on the Internet or going through books in your library and may have the curiosity to know which language it is. This NLP Project is all about quenching your curiosity only. to build your own language identifier. 

Method: This project will involve using the Language Detection dataset for training your machine learning/deep learning algorithm. This dataset has two columns: text and language. After performing text preprocessing methods, you can use your preferred algorithm to predict the correct target variable of language for a given text. If you want to implement this NLP project in Python, we suggest you use libraries like Pandas, Numpy, Seaborn, NLTK, and Matplotlib.

NLP Projects Idea #2 Image-Caption Generator

Consider you are given a system and asked to describe it. It sounds like a simple task but for someone with weak eyesight or no eyesight, it would be difficult. And that is why designing a system that can provide a description for images would be a great help to them.

Method:  This advanced NLP project is a slightly complex one but is equally interesting. One must have a fair idea of deep learning algorithms and image processing techniques as well to implement this project. So, if you haven’t tried them yet, this project will motivate you to understand them. You will have to first use image processing and deep learning algorithms to label objects in the image and then convert that information into relevant sentences through NLP methods.

Recommended Reading: Top 10 Deep Learning Algorithms in Machine Learning

NLP Projects Idea #3 Homework Helper

This is a very cool NLP project for all the parents out there who struggle with helping their children in completing complicated tasks assigned as homework to their kids. The reason is simple : they feel like they’re too old for it and have forgotten most of the things. But dear parents don’t worry, NLP is here to help. By designing a simple NLP-based app, you can help your kids with their homework.

Method: For this NLP based project, you can use pdfs by NCERT or by any other freely available publication house as your dataset. You can implement NLP methods to analyze the data and then use specific machine learning or deep learning algorithms to find answers/relevant text to the questions asked by the user.

GitHub NLP Projects

In this section, you will get to explore NLP github projects along with the github repository links.

NLP Projects Idea #1 Analyzing Speech Emotions

In this project, the goal is to build a system that analyzes emotions in speech using the RAVDESS dataset. It will help researchers and developers to better understand human emotions and develop applications that can recognize emotions in speech.

The project uses a dataset of speech recordings of actors portraying various emotions, including happy, sad, angry, and neutral. The dataset is cleaned and analyzed using the EDA tools and the data preprocessing methods are finalized. After implementing those methods, the project implements several machine learning algorithms, including SVM, Random Forest, KNN, and Multilayer Perceptron, to classify emotions based on the identified features.

GitHub Repository: Speech Emotion Analyzer by Mitesh Puthran 

NLP Projects Idea #2 Detecting Paraphrases

This project is perfect for researchers and teachers who come across paraphrased answers in assignments. You will work on building a system that identifies whether two sentences are paraphrases of each other or not.This project will also be helpful for researchers and developers as it will allow them to build systems that can recognize paraphrases and improve natural language processing applications.

The project uses the Microsoft Research Paraphrase Corpus, which contains pairs of sentences labeled as paraphrases or non-paraphrases. After extracting the relevant features through feature selection methods, the machine learning algorithms including logistic regression, support vector machines, decision trees, and random forests ,.are trained to classify sentence pairs as paraphrases or non-paraphrases based on the identified features.

GitHub Repository: Paraphrase Identification by Wasiahmad

NLP Open Source Projects

This heading has the list of NLP projects that you can work on easily as the datasets for them are open-source.

NLP Projects Idea #1 Recognising Similar Texts

This NLP project is a must for any NLP enthusiast. It was launched as a challenge on Kaggle about 4 years ago. If you have ever visited the Quora website, you would have noticed sometimes, two questions on the website have the same meaning but different answers. This creates a problem as the website wants its readers to have access to all answers that are relevant to their questions. In order to solve this problem, Quora launched the Quora Question Pairs Challenge and asked the Data Scientists to come with a solution for identifying questions that have a similar intent. The idea is to present all the answers to their readers for all the questions that may look different but have the same intent.

Method: In this NLP Project, you can use bar plots and histograms to visualize textual data before using any machine learning algorithms on it. You will have to perform lemmatization, remove stop words, convert text to numbers using vectorization techniques. After that, you should use various machine learning algorithms like logistic regression, gradient boosting, random forest, and grid search CV for tuning the hyperparameters. To know the step-by-step solution for this, click NLP Projects - Kaggle Quora Question Pairs Solution.

NLP Projects Idea #2 Inappropriate Comments Scanner

The twenty-first century is the age of social media. On one hand, many small businesses are benefiting and on the other, there is also a dark side to it. Because of social media, people are becoming aware of ideas that they are not used to. While few take it positively and make efforts to get accustomed to it, many start taking it in the wrong direction and start spreading toxic words. Thus, many social media applications take necessary steps to remove such comments to predict their users and they do this by using NLP techniques.

Method: The dataset for this project is freely available on Kaggle. You can use this dataset to classify the comments as toxic and non-toxic. For this project, you will have to first use textual data preprocessing techniques. After that, you must perform basic NLP methods like TF-IDF of converting textual data into numbers and then use machine learning algorithms to label the comments. 

NLP Projects Idea #3 GPT-3

GPT-3 (Generative Pre-trained Transformer 3) is a state-of-the-art natural language processing model developed by OpenAI. It has gained significant attention due to its ability to perform various language tasks, such as language translation, question answering, and text completion, with human-like accuracy. 

GPT-3 is trained on a massive amount of data and uses a deep learning architecture called transformers to generate coherent and natural-sounding language. Its impressive performance has made it a popular tool for various NLP applications, including chatbots, language models, and automated content generation. 

NLP Projects Idea #4 BERT

BERT (Bidirectional Encoder Representations from Transformers) is another state-of-the-art natural language processing model that has been developed by Google. BERT is a transformer-based neural network architecture that can be fine-tuned for various NLP tasks, such as question answering, sentiment analysis, and language inference. Unlike traditional language models, BERT uses a bidirectional approach to understand the context of a word based on both its previous and subsequent words in a sentence. This makes it highly effective in handling complex language tasks and understanding the nuances of human language. BERT has become a popular tool in NLP data science projects due to its superior performance, and it has been used in various applications, such as chatbots, machine translation, and content generation. 

NLP Projects Idea #5 Hugging Face 

Hugging Face is an open-source software library that provides a range of tools for natural language processing (NLP) tasks. The library includes pre-trained models, model architectures, and datasets that can be easily integrated into NLP machine learning projects. Hugging Face has become popular due to its ease of use and versatility, and it supports a range of NLP tasks, including text classification, question answering, and language translation.

One of the key advantages of Hugging Face is its ability to fine-tune pre-trained models on specific tasks, making it highly effective in handling complex language tasks. Moreover, the library has a vibrant community of contributors, which ensures that it is constantly evolving and improving. Check out the official website of Hugging Face to know more.

If you enjoyed reading about these NLP project ideas and are looking for more NLP Data Science projects ideas with solutions then check out our repository: Top NLP Projects | Natural Language Processing Projects.

FAQs

What are NLP tasks?

NLP comprises multiple tasks that allow you to investigate and extract information from unstructured content. These tasks include Stemming, Lemmatisation, Word Embeddings, Part-of-Speech Tagging, Named Entity Disambiguation, Named Entity Recognition, Sentiment Analysis, Semantic Text Similarity, Language Identification, Text Summarisation, etc.

How do I start an NLP Project?

There are five steps you need to follow for starting an NLP project-.
1) Lexical analysis- It entails recognizing and analyzing word structures. The text is divided into paragraphs, phrases, and words using lexical analysis.
2) Syntactic analysis- It examines grammar, word layouts, and word relationships.
3) Semantic analysis retrieves all alternative meanings of a precise and semantically correct statement.
4) Discourse integration is governed by the sentences that come before it and the meaning of the ones that come after it.
5) Pragmatic analysis- It uses a set of rules that characterize cooperative dialogues to assist you in achieving the desired impact.

How to handle text data preprocessing in an NLP project?

Text data preprocessing in an NLP project involves several steps, including text normalization, tokenization, stopword removal, stemming/lemmatization, and vectorization. Each step helps to clean and transform the raw text data into a format that can be used for modeling and analysis.

How to evaluate the performance of an NLP model?

The performance of an NLP model can be evaluated using various metrics such as accuracy, precision, recall, F1-score, and confusion matrix. Additionally, domain-specific metrics like BLEU, ROUGE, and METEOR can be used for tasks like machine translation or summarization.

About the Author

Manika

Manika Nagpal is a versatile professional with a strong background in both Physics and Data Science. As a Senior Analyst at ProjectPro, she leverages her expertise in data science and writing to create engaging and insightful blogs that help businesses and individuals stay up-to-date with the

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