Goglides Dev 🌱

Goglides Dev 🌱 is a community of amazing users

Shaping the future of IT, one connection at a time.

Create account Log in
Cover image for Top 10 Exciting NLP Project Ideas to Try Out in 2023!
Debasmita Ghosh
Debasmita Ghosh

Posted on

Top 10 Exciting NLP Project Ideas to Try Out in 2023!

NLP ( Natural Language Processing) became a key part of the modern technological landscape that enables computers to understand, interpret, and generate human language. Take a look at this blog to explore the top project ideas that will give you a better understanding of this language. By getting that immersive understanding, you’ll not only get comprehensive knowledge but also come to know about its revolutionary impact on the data analysis landscape. Keep on reading.

1. Named Entity Recognition (NER)

It’s a fundamental task in NLP that aims to recognize and classify items ( for instance, people’s names, organizations, and dates) from the provided texts. This research creates a NER system that can be used for identifying names in texts, helping to extract information from the structured data. It needs a labeled data that comes with texts with different annotated identities.
CoNLL-2003, OntoNotes, and Open Multilingual Wordnet are some of its commonest data sets.
The NER system can recognize and classify named entities in the provided text accurately. It can be used properly for information extraction tasks, sentiment analysis, and other NLP applications to get an insight from unstructured data.

2. Automatic and Accurate Text Summarization

Short and summarized news and articles are getting high popularity in this fast-paced world when we hardly have time and patience to go through long news and articles. The demand for the InShorts mobile apps, ( apps that summarize the long news articles into just 60 words) proves this clearly. It’s an amazing product idea based on the NLP algorithm. For these projects, using algorithms like cosine similarity will be advisable which would help to pick the relevant sentences that will be useful for the summary.

3. Spam Identification

Probably you did not forget the not-so-old good days when your mailbox used to get filled with lots of junk emails. Now surely, we have succeeded in coming out of it, and a big credit for it goes to NLP. With the help of the NLP algorithms, email services can identify spam emails, keeping your mailbox free of junk mail. For this project, you need to collect a dataset of emails and then use the email body for giving training to your algorithm.

4. Deepfake Detection

Deepfake detection ( a process of detecting fake videos or images that were created using deep learning) got super-easy using NLP. The goal of this NLP project is to create a deep-learning-based model that can identify manipulated videos or images that can safeguard the media identity and give protection against the probable misuse of data.

5. Detecting Emotions

Another significant NLP project that gets popularity is emotion detection which discerns and comprehends sentiment within textual content. Leveraging this technology finds applications in sentiment analysis, customer service, and seamless human-computer interaction.

The purpose of this project is to craft an NLP system adept at interpreting emotions like joy, sadness, and fury from spoken or written expressions. Such projects need annotated datasets, preprocessing for feature extraction, and refining data for emotion classification. The model also can recognize emotions from speech, gauging accuracy through metrics, and its potential to elevate user experiences across NLP domains.

6. TTS & STT

Text-to-speech (TTS) and Speech-to-Text (STT) are integral aspects of Natural Language Processing ( NLP) that enable seamless human-machine communication. TTS converts written text to human-like voice, while STT transforms spoken words into text, enhancing accessibility and interaction.

The main motto of this specific project is to develop a bidirectional NLP system proficient in both functions. TTS projects need a dataset of paired text audio for training, while STT projects require audio data with transcriptions. The system's outcome will encompass lifelike speech synthesis and accurate speech-to-text transcription, amplifying user interfaces, and expanding the genre of voice-driven applications.

7. Analyzing Market

During your grocery-buying trips to the supermarkets, you’ll often find shelves containing candies and chocolates near billing counters. It’s a wise and strategic move by the market owners considering buyers’ psychology.

Shoppers often resist temptations at the start, but willpower vanishes as they queue up for billing. Waiting at the counter, they're primed to notice these treats. It's not just about sweet cravings, though. This insight into human behavior is the essence of Market Basket Analysis, a clever NLP project.

This project aims to show the importance of market basket analysis for your company. Here you can get an understanding of several association rules and apriori and the Fp Growth algorithm ( two Join-Based algorithms and a Tree-Based algorithm used for frequent itemset mining).

8. Chatbot Creation

AI-driven chatbots have proven their excellence in customer service, virtual assistance, and various domains and using NLP chatbot creation becomes easier. The main goal of this project is to create conversational AI agents that can hold contextually apt and interactive conversations with different users in varied domains.

To train chatbots, you need a conversational dataset that consists of user-bot interactions and corresponding responses. The intention of this project is to offer an enhanced user experience by providing a personalized interaction.

9. Creating Quote Generators

It's another inspiring and creative NLP project that creates motivating code generators. This project aims to develop an NLP model that can generate inspiration for keeping users motivated. To give the proper training to the code generator a dataset with codes with relevant associate keywords is needed.

10. Diagnosing Diseases

NLP projects are ideal for identifying and analyzing the medical history of several patients. It can find out the severity of several symptoms, potential risk factors, etc. NLP can extract information from different sources like electrical health records or unstructured clinical data. Information NLP uses include demographics, earlier medical history of patients, treatment plans, etc. NLP works by identifying signals or patterns in text data that might indicate any specific disease.

Final Words

If you are excited to unlock the dynamic world of NLP and data analysis and want to try some great project ideas, these can be good options. Here we mentioned some projects that cater to the diversified skillets. Whereas NER helps in showing basic skills deepfake protection showcases your advanced skillset.

Some Common FAQs on NLP Projects

What Do NLP Tasks Mean?
You may consider NLP tasks like a treasure map for words! It's a world of tasks that let you explore secrets hidden in messy and unstructured texts. These tasks include Stemming Named Entity Recognition ( NER), Lemmatisation Sentiment Analysis, Semantic Text Similarity, Language Identification, Text Summarisation, Word Embeddings, Part-of-Speech Tagging, etc.

What are the Right Ways to Handle Data Preprocessing in NLP Projects?
Text data preprocessing in an NLP project involves several steps that include text normalization, tokenization, stopword removal, stemming/lemmatization, and vectorization. Each step over here cleans and transforms the raw text data into a format that is relevant for modeling and analysis.

What are the Different Examples of NLP?
NLP wears many hats! It can understand feelings (sentiment analysis), chat like a good friend (chatbots), translate languages (machine translation), understand speech (speech recognition), sort texts (text classification), and pick out names (named entity recognition). You'll find NLP helping in varied sectors, It's like a wizard for words!

How to Get Started with an NLP Project?
Of course, at the start, you need to explore its basics, and familiarity with NLP tools like NLTK, spaCy, TensorFlow, or PyTorch is a must. In the next step, choose a project that you find interesting. Then start to collect relevant data that fits the bill and play around with different models and tricks. It's like cooking: prep, pick your dish, gather ingredients, and experiment for a great project!

Discussion (0)