5 Project Topics in Natural Language Processing You Should Know [2024]

Nowadays, most industries are using Natural Language Processing. There are various jobs available in Natural Language Processing. Many companies are hiring NLP Engineers. So if you want to enhance your portfolio and strengthen your knowledge in Natural Language Processing, check out these 5 project topics in Natural Language Processing. Because the more you practice with different Natural Language Processing projects, the more experience you gain.

That’s why in this article, I am gonna share 5 project topics in Natural Language Processing, that will help you to test your NLP knowledge. So without any further ado, let’s get started-

5 Project Topics in Natural Language Processing

1. Sentiment Analysis 

In natural language processing, sentiment analysis is used to interpret the sentiments and classify them as positive, negative, and neutral. Sentiment analysis is used in various domains, especially in business. Businesses are using sentiment analysis to find the opinions of their customers by using customer reviews to improve their services.

I am also using Sentiment analysis on social media posts to predict the depression level. Because Depression is a serious issue in our society and people are sharing their views on various social media platforms such as Facebook, Twitter, etc.

Many Political parties are using sentiment analysis to plan their election campaigns. So if you want to implement sentiment analysis, you can find the datasets from these websites-

Datasets For Sentiment Analysis–

1. Twitter US Airline Sentiment– Kaggle
2. Paper Reviews Data Set– UCI
3. Sentiment Lexicons for 81 Languages– Kaggle
4. Amazon product data
5. Stanford Sentiment Treebank

2. Customer Support Bot

When you have any query with any product, then you complain to customer support. So when you send a message with your query, you get a reply within a few seconds. So this a Customer Support Bot, who understands your language by processing and then reply to your query.

You can check examples of chatbots in eCommerce, healthcare, entertainment, and customer service in this article- The Best Chatbot Examples and Awesome Chatbot Ideas That You Can Borrow

And you can find Chatbot Datasets from these websites-

Chatbot Datasets–

1. The WikiQA Corpus
2. Relational Strategies in Customer Service Dataset
3. Customer Support on Twitter
4. ConvAI2 Dataset

3. Speech Recognition

This is the most popular application of NLP. You are aware of Google’s assistant, Amazon Alexa, Apple’s Siri, and Cortana. When you give any voice commands to them, they process with your language and try to give the best answer to your question.

In speech recognition, you can work on Recommender System for Music that analyzes audio to predict user behavior and based on user behavior suggest music. Spotify has already implemented such recommender systems to enhance their user experience. 

You can also perform the separation of audio sources that distinguish different types of audio source signals present in the midst of signals. Deep Learning can be used to perform the separation of audio sources. You can check this app as an example of the separation of audio sources.

Now, let’s see where you can find the datasets for speech recognition project-

Datasets for Speech Recognition-

1. Google AudioSet
2. LibriSpeech ASR corpus
3. TED-LIUM Corpus
4. Gender Recognition by Voice
5. VoxCeleb

4. Text Summarization

Text Summarization is a method to create a short, accurate and fluent summary of a longer text document to save readers time and effort.

You can use text summarization on social media marketing to break down the content of whitepapers, e-books, blogs and make it sharable on social media sites like Twitter or Facebook.

Or you can use text summarization in search marketing and SEO to analyze dozens of search results, understand shared themes and skim the most important points.

Now let’s see where you can find the datasets for text summarization-

Datasets for Text Summarization–

1. BBC News Summary
2. Information Retrieval Resources
3. Product Title Summarization
4. Large-Scale Chinese Short Text Summarization Dataset
5. News Summary

5. Machine Translation

Many of us use Google Translation to translate from one language to another. And this is possible because of NLP. You can build a machine translation application that can translate textual, audio, and image files from a source language into a target language.

You can read this research paper on Machine Translation for Government Applications.

Now let’s see where you can get datasets for Machine translation-

Datasets for Machine Translation-

1. Japanese-English Bilingual Corpus
2. Chinese-French Text
3. French-Arabic Newspapers
4. German-English Text

And here the list end. I hope these project topics in Natural Language Processing will definitely help you to enhance your portfolio and strengthen your knowledge in Natural Language Processing. I would suggest you bookmark this article for future referrals. Now it’s time to wrap up.

Conclusion

In this article, I tried to cover 5 project topics in Natural Language Processing. If you have any doubt or questions, feel free to ask me in the comment section.

All the Best!

Enjoy Learning!

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Though of the Day…

‘ Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

– Henry Ford