Five interesting NLP projects for beginners in 2023

Highlights

The natural language processing (NLP) area is quite intriguing. NLP initiatives and applications are already pervasive in daily life. From chatbots (Amazon Alexa) to sentiment analysis (Hubspot's function for customer feedback analysis), language translation and recognition (Google Translate), spelling check (Grammarly), and much more.

NLP is an AI component that deals with the interaction between humans and computers. It can be challenging to locate NLP projects that meet your learning needs as a newbie in software development.

So, to help you start, we've compiled a list of examples. If you're new to machine learning, the most significant thing you can do is work on some NLP projects.

Sentiment analysis

It is one of the most popular NLP tasks that practically every NLP Research Engineer has completed. Businesses use it to track client product feedback, making it popular. If most reviews are positive, the companies are on the right course. Moreover, if the majority of evaluations generated by this NLP Project are deficient, the corporation can make efforts to improve the product.

Method:

The initial stage in building the Sentiment Analysis system would be to apply EDA to textual data. After that, you will need to utilize text data processing techniques to extract pertinent information from the data and eliminate irrelevant material. The subsequent stage would examine the reviewer's sentiment by identifying key terms.

GitHub: bentrevett/pytorch-sentiment-analysis

Conversational Bots: ChatBots

As we indicated at the beginning of this article, most tech companies today use Chatbots, which are conversational bots, to communicate with their consumers and handle issues. It is an excellent method for both customers and businesses to save time. For example, customers are linked with a customer service representative only if human intervention is required after the user has provided all the information requested by the bot.

Method:

This project will teach you how to utilize the NLTK Python package for text classification and preprocessing. You will also investigate the Python implementation of Tokenization, lemmatization, and Parts-of-Speech tagging.

GitHub: Turing-Project/AntiFraudChatBot

Topic Identification

It is a fundamental NLP project that requires an in-depth understanding of NLP algorithms. The objective is to label a document with a suitable subject using applicable algorithms. Using this NLP project to categorize customer reviews is an excellent real-world use of this NLP project. The companies can then use customer feedback themes to determine the most critical areas for improvement.

Method:

This project will expose you to techniques for working with textual data and regex. In addition, you will learn how to turn textual data into vectors using techniques such as TF-IDF and Count vectorizer.

GitHub: vibuverma/steam-reviews-topic-modeling

Text Processing and Classification

Natural Language Processing (NLP) might be tough to comprehend for beginners in machine learning. Therefore, one must begin with easy projects to quickly learn NLP and progressively increase the difficulty level. Accordingly, if you are a novice seeking a fundamental and beginner-friendly NLP project, we suggest you begin with this one.

GitHub: msgi/nlp-journey

Language identifier

It is an outstanding NLP assignment for novices. The process of detecting the language of a specific text entails sifting through many dialects, slang, and common vocabulary between languages and using numerous languages on a single page. However, machine learning makes this process much simpler. Using Facebook's fastText paradigm, you can design your language identifier. The model extends the tool word2vec and employs word embeddings to comprehend a language.

GitHub: axa-group/nlp.js