TOP NLP Projects in 2022 (2022)

TOP NLP Projects in 2022 (1)

  • Introduction
  • Top NLP Projects

Introduction

Introduction to Natural language processing: NLP is an exciting and upcoming field in Artificial Intelligence. NLP finds part in the portfolio of products for most of the companies working in the field of data sciences. NLP can be seen in action in all conversational voice assistants (Amazon Alexa, Apple Siri) to sentiment analysis (Hubspot’s customer feedback analysis feature, Amazon recommendation), language recognition and translation (Google Translate, IBM Watson), spelling correction (Grammarly), autocomplete (Google Gmail) etc.

Top NLP Projects

Find NLP application in the following systems

  1. Sentiment Analysis
  2. Text Classification
  3. Chatbots & Virtual Assistants
  4. Text Extraction
  5. Machine Translation
  6. Text Summarization
  7. Market Intelligence
  8. Auto-Correction of text
  9. Intent Classification
  10. Urgency Detection
  11. Speech Recognition

Before we discuss NLP project ideas, let us delve into NLP detection, which is defined as computational processing (pre-processing, transformation, manipulation etc.) of natural language by a software program. In common man’s language, Natural language refers to the humans communicating with each other. NLP also means understanding complete human utterances and giving suitable responses to them.

(Video) Top 11 NLP Projects for Portfolio | Best NLP Projects

Scientifically, Natural Language Processing or NLP can be defined as an interdisciplinary subject comprising of statistics, linguistics, Artificial Intelligence and computer programming that enables the computing machines to read, understand and derive meaning from human languages. NLP is increasingly adopted in our day to day life to solve real-world problems. Some of its applications are as follows:

NLP can assist doctors to recognize and predicting diseases based on electronic health records and patients’ speech. It is of less use for commonplace diseases but is of great help for life-threatening diseases such as cardiovascular diseases, various forms of cancer, mental disorders, renal disorders and schizophrenia. For example, Amazon Comprehend Medical is an NLP based service that finds out disease conditions, medications and treatment outcomes from patient notes, clinical trial reports and other electronic health records.

Organizations can determine customer trends and customer preferences and buying habits by identifying and extracting information from sources like social media and carrying out sentimental analysis. This sentiment analysis can help a marketer mine customers’ choices and their decision drivers.

Companies such as Google and Amazon have increasingly used NLP in their software and hardware products to enable them to provide personalised touch i.e. understand customer commands spoken in their home or native language, give custom curate greetings, understand common phrases, carry out personalized searches etc. E.g. IBM cognitive assistant software works like a personalized search engine by learning all about you and then reminds you of a name, a song, or anything you can’t remember the moment you need it to.

(Video) The Top 10 BEST ML & NLP Projects to help you level up

Companies have increasingly adopted NLP in their customer services by replacing a live representative with AI-powered automated chatbots. A chatbot learns everything about the product it is representing, all common problems or issues faced by the product’s customers and common information sought. Chatbot also has NLP modules inbuilt to recognize natural language spoken by customers. It can then guide any customer who contracts with a problem, to a satisfactory resolution. This saves huge operational costs and each interaction add to the chat bot’s training thereby making it more efficient.

Companies are increasingly using NLP to solve common nuances such as spam detection and fake news detection. Google’s Gmail filter and classify your emails with NLP by analyzing text in emails that flow through their servers and tagging spam for you so that you do not have to waste your time on them. BBC, for instance, uses NLP powered system to determine if a news source is accurate or politically biased by calculating its trust score and then accepting it if it is above a threshold.

NLP has also made its way into corporate recruitment. NLP powered systems are used in both the search and selection phases of talent recruitment, identifying the skills of potential hires and cherry-picking prospects before they become active on the job market.On a different note, check out the NLP Customer Experience course.

NLP’s star application is voice-based intelligence. Amazon’s Alexa and Apple’s Siri are examples of intelligent voice-driven interfaces that use NLP to detect and follow user commands as diverse as searching a nearby ATM to opening an AI-powered tube light in the house.

(Video) NLP Projects & Research Ideas 2021

NLP is also paving its way in financial trading. NLP is being used to track news, reports, comments about possible mergers between companies, everything can be then incorporated into a trading algorithm to generate accurate share price.

TOP NLP Projects in 2022 (2)

Writing real-world NLP projects is simply the best way to hone one’s NLP skills and transform theoretical knowledge into a valuable practical experience that could be showcased to the external world by a portfolio of projects. This will directly translate to professional advantage when you will apply for any NLP-related job. Your portfolio of projects will prove your “NLP proficiency” to the recruiter and help you edge out other candidates.

Some of the top NLP project ideas are as follows:

Sentiment analysis for the product: This type of project can greatly help a product manager to make strategic decisions related to the product under consideration. It will firmly place you as an NLP specialist because it has all components of an NLP theory. Here, you aim to find out how customers evaluate competitor products, i.e. what they like and dislike. Technically, Sentiment analysis is contextual mining of text which identifies and extracts subjective information in an input source to get meaningful insights on which business decisions can be made.

(Video) Challenges in NLP research in 2022: large models, multi-modality and datasets | Thomas Wolf

Predictive text analysis: autocorrect and auto corrective features are an increasing part of any word processing software. They either finish a word or throw suggestions based on previous learning and present context. Predictive text will customize itself to the regional and natively used language. You can create a project wherein the user can input text or a file and the NLP based code can correct errors. You can also provide a text box where the user can type and the code can read and predict the next word based on an ensemble of NLP techniques.

Moderation system: In this project, you need to moderate comments posted by users as normal or toxic. A toxic comment uses offensive, sexual, religiously hurting, cast or creed bashing words etc. This has practical significance since most of the public forums have this feature built-in. Here you have to use various NLP word processing as well as transfer learning techniques to detect toxic comments posted in the native language.

Language translators: This is the most used application of NLP and can showcase your proficiency in NLP. Most languages do not get translated word to word and have different orders for sentence structure which confuses any translation service. In this project, you have to use NLP to translate languages more accurately and present grammatically-correct results. Moreover, you can explore various NLP based tools to recognize the language based on inputted text and translate it thereafter.

Email filters: Spam detectors and email filters are the two most sought applications of NLP ever since NLP become available. So you can also create a project on a spam detector wherein you will read an email, use various properties and rules to predict if the given email is spam or not. You can auto-learn your model so that it can improve with more data.

(Video) Twitter Sentiment Analysis by Python | best NLP model 2022

Document Similarity scorer: In this project, you will create a project that will take as input a set of documents and be given a grid giving the similarity score of one document with the other. This finds practical application in websites such as Quora and Chegg.

Quora is a question and answer platform where people ask questions and people only provide answers to them. Thus, the entire content on the website is generated by users; serve to make people learn from each other’s experiences and knowledge. But such platforms have problems of duplicity. Since there is no check on question posted, it is often found to be nearly a duplicate of an existing question. So this project, will compare each question with other questions in its category and give a similarity score ranging from 0.0 (no duplicity at all) to 1.0 (complete duplicate). The business benefit for this project is that based on score, we can tag all similar questions with the original question and close them so that people focus on the only original question.

Text Summary creator: In this protection, you will take as input a long text and give as output a crafted summary about this text. Text summarization is one of the most interesting problems in NLP because it is very hard and time-consuming to manually extract the summary of a large document of text. In this project, you could use different traditional and advanced methods to implement automatic text summarization, and then compare the results of each method to conclude which is the best to use for your corpus.

TOP NLP Projects in 2022 (3)

FAQs

Which industry uses NLP the most? ›

Since then NLP is being progressively integrated into computer science and artificial intelligence to develop systems and software, capable of processing human languages.
...
Let us take a look at some of the sectors that get benefit from NLP.
  1. Health Care. ...
  2. Finance. ...
  3. Education. ...
  4. Business. ...
  5. IT and Data Science.
16 Dec 2021

What are possible future applications of NLP? ›

NLP is widely used in healthcare as a tool for making predictions of possible diseases. NLP algorithms can provide doctors with information concerning progressing illnesses such as depression or schizophrenia by interpreting speech patterns. Still, psychiatry is not the only field of medicine that NLP finds use in.

Which model is best for NLP? ›

GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic.

Is there demand for NLP? ›

The natural language processing (NLP) market is rapidly growing, and according to Statista it's expected to grow to over $43 billion by 2025. With the industry experiencing such large growth, professionals skilled in natural language processing are in high demand.

Why is there a demand for NLP? ›

NLP has enabled predictive analytics and helped minimize population health concerns. Moreover, the adoption of natural language processing in healthcare is rising because of its recognized potential to search, analyze, and interpret massive amounts of patient datasets.

How big is the NLP market? ›

Europe is the second-largest market for natural language processing. It is expected to reach an estimated USD 23 billion by 2030, growing at a CAGR of 26.4%.
...
Report MetricDetails
Market SizeUSD 91 Billion by 2030
CAGR27% (2022-2030)
Historical Data2019-2020
Base Year2021
8 more rows
11 Aug 2022

Does NLP have a future? ›

The future of Natural Language Processing (NLP) is a little bit unpredictable, but it is clear that it will be a part of our daily lives in the next few years. NLP is the process of understanding natural human language. In other words, it is the ability for machines and computers to understand human language.

Why is NLP hard? ›

Why is NLP difficult? Natural Language processing is considered a difficult problem in computer science. It's the nature of the human language that makes NLP difficult. The rules that dictate the passing of information using natural languages are not easy for computers to understand.

What is the scope of NLP in today's scenario? ›

NLP has a broad scope, with so many uses in customer service, grammar check software, business marketing, etc. If you are interested in computing and languages, then NLP is a good career option for you. You can consider career options like NLP Engineer, NLP Architect, etc.

Is T5 better than BERT? ›

But the differentiator that truly sets T5 apart from BERT-style models is that it does not output a label or a span of the input to the input sentence, but the output is a text string as well.

What algorithms are used in NLP? ›

NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statistical inference.

Is Lstm better than BERT? ›

Our experimental results show that bidirectional LSTM models can achieve significantly higher results than a BERT model for a small dataset and these simple models get trained in much less time than tuning the pre-trained counterparts.

How do you become a pro in NLP? ›

  1. Step 1: Programming. I was really hesitant whether to put programming as the first step of the second one. ...
  2. Step 2: Math, Statistics, and Probability. ...
  3. Step 3: Text Preprocessing. ...
  4. Step 4: Machine Learning Basics. ...
  5. Step 5: NLP Core Techniques. ...
  6. Step 6: Future Natural Language Processing.
9 Feb 2021

Is learning NLP easy? ›

Or is NLP hard to learn? NLP is easy to learn if you have a touch of curiosity, courage, ambition, discipline and openness. Let's assume you're learning NLP to be effective using it on yourself, with your colleagues and your clients.

What should I learn before NLP? ›

That will include advanced calculus, linear algebra, probability and statistics, and differential equations. It is needed to understand machine learning and deep learning algorithms that are used along with NLP techniques. Ability to code in one of the popular programming languages like C/C++, Python, R, Java.

Do chat bots use NLP? ›

The chatbots of today are sleek and sophisticated. In fact, with the use of machine learning technology, they can even feel human. These AI-powered chatbots use a branch of AI called natural language processing (NLP) to provide a better user experience.

Do all chatbots use NLP? ›

Like the previous example with Amazon's Alexa, chatbots would be able to provide little to no value without Natural Language Processing (NLP). Natural Language Processing is what allows chatbots to understand your messages and respond appropriately.

Which algorithm is used in chatbot? ›

Among other things, some of the most popular algorithms used by conventional Chatbots are Naïve Bayes, Decision Trees, Support Vector Machines, Recurrent Neural Networks (RNN), Markov Chains, Long Short Term Memory (LSTM) and Natural Language Processing (NLP).

Is NLP a good career? ›

The financial value of it as a career option with respect to how much you will earn as an NLP engineer is on the upper end of the average income of engineers. So if it is important to you to choose a job that would make a good source of income then it is definitely worth it.

How much does an NLP practitioner earn? ›

Those that succeed earn between $25,000 and $80,000 a year with, with a tiny number earning much, much more. Although failing can be a tough experience, many learn from what's happened and move on to very successful careers in related disciplines.

How do I become a NLP developer? ›

The answer is an associate or bachelor's degree in a related field, such as engineering, data science, or computer science. A master's degree in a related subject such as data science or artificial intelligence or a Ph. D. with a focus in NLP is likely to be preferred or required for high-level positions.

What Neuro Linguistic Programming? ›

Neuro-linguistic programming (NLP) is a psychological approach that involves analyzing strategies used by successful individuals and applying them to reach a personal goal. It relates thoughts, language, and patterns of behavior learned through experience to specific outcomes.

How big is the artificial intelligence market? ›

The global Artificial Intelligence market size was valued at US$ 93.8 billion in 2021 and is anticipated to grow at a CAGR of 38.9% during forecast period 2022 to 2030.

How is natural language processing used in the BFSI sector today? ›

The insurance and financial services industries are prime candidates for natural language processing (NLP) technology. NLP can help banks, insurers, and other financial institutions automate processes, improve customer service, and make better decisions.

Is sentiment analysis a good project? ›

Sentiment analysis is critical because it helps businesses to understand the emotion and sentiments of their customers. Companies analyze customers' sentiment through social media conversations and reviews so they can make better-informed decisions.

How can NLP be used in marketing? ›

NLP is often used to gain a qualitative understanding of the “why” and “what” of a situation, and enables users to make more insightful decisions. In Marketing Analytics, NLP can be used to understand your audience's intentions so that you can create smarter, more efficient marketing strategies.

What are NLP algorithms? ›

NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP can rely on machine learning to automatically learn these rules by analyzing a set of examples (i.e. a large corpus, like a book, down to a collection of sentences), and making a statistical inference.

What is NLP practitioner? ›

An NLP Practitioner is a highly resourceful and professional coach who uses the NLP technique to help others. The technique entails using the brain deliberately (in terms of procedures and interventions) to achieve the desired results.

What is a good sentiment score? ›

The score indicates how negative or positive the overall text analyzed is. Anything below a score of -0.05 we tag as negative and anything above 0.05 we tag as positive. Anything in between inclusively, we tag as neutral.

Is sentiment analysis data mining? ›

Sentiment analysis, also referred to as opinion mining, is an approach to natural language processing (NLP) that identifies the emotional tone behind a body of text. This is a popular way for organizations to determine and categorize opinions about a product, service, or idea.

How big is the NLP market? ›

Europe is the second-largest market for natural language processing. It is expected to reach an estimated USD 23 billion by 2030, growing at a CAGR of 26.4%.
...
Report MetricDetails
Market SizeUSD 91 Billion by 2030
CAGR27% (2022-2030)
Historical Data2019-2020
Base Year2021
8 more rows
11 Aug 2022

How can NLP help in sales? ›

1) NLP Helps Salespeople to Understand Their Clients

NLP can help those involved in sales to become more skilled at communicating. By helping those trained in it to be better communicators, NLP helps salespeople to identify their clients' needs and to meet those needs more easily.

Why is NLP useful in e marketing? ›

But, how does NLP fit the bill in effective digital marketing? NLP makes it easy for you to process tons of language data collected from Google search, website visits, or social media pages, and then use the information to improve your marketing strategies across these channels.

Is NLP AI or ML? ›

Natural Language Processing (NLP) is a branch of Artificial Intelligence (AI) that enables machines to understand the human language. Its goal is to build systems that can make sense of text and automatically perform tasks like translation, spell check, or topic classification.

Why is NLP hard? ›

Why is NLP difficult? Natural Language processing is considered a difficult problem in computer science. It's the nature of the human language that makes NLP difficult. The rules that dictate the passing of information using natural languages are not easy for computers to understand.

How much does an NLP practitioner earn? ›

Those that succeed earn between $25,000 and $80,000 a year with, with a tiny number earning much, much more. Although failing can be a tough experience, many learn from what's happened and move on to very successful careers in related disciplines.

Is NLP a Recognised qualification? ›

Sometimes yes, mostly no. The benefit of learning to use NLP is that it will help you produce significantly better results in a variety of situations. A number of leaders and bosses know this and will treat NLP as a recognised qualification.

How much does NLP cost? ›

How much does NLP training cost? You can expect to pay somewhere in the region of $3000 to $4500 for an NLP certification program.

Videos

1. Twitter API with Python 2022 | NLP Project Series - Part 1/3 | Tweepy | Sentiment Analysis
(SKILLCATE)
2. The 8 Biggest Artificial Intelligence (AI) Trends In 2022
(Bernard Marr)
3. Let's Create NLP Web App | End to End Project - @HuggingFace | Data Summarization | Hindi 2021
(End to End)
4. Top 5 Machine Learning Project Ideas for 2022 to Improve your Resume
(Ishan Sharma)
5. Transformers in Natural Language Processing | Data Science Summer School 2022
(Hertie School Data Science Lab)
6. 500+ Machine Learning And Deep Learning Projects All At One Place
(Krish Naik)

You might also like

Latest Posts

Article information

Author: Trent Wehner

Last Updated: 08/19/2022

Views: 5417

Rating: 4.6 / 5 (56 voted)

Reviews: 95% of readers found this page helpful

Author information

Name: Trent Wehner

Birthday: 1993-03-14

Address: 872 Kevin Squares, New Codyville, AK 01785-0416

Phone: +18698800304764

Job: Senior Farming Developer

Hobby: Paintball, Calligraphy, Hunting, Flying disc, Lapidary, Rafting, Inline skating

Introduction: My name is Trent Wehner, I am a talented, brainy, zealous, light, funny, gleaming, attractive person who loves writing and wants to share my knowledge and understanding with you.