nlp - Nomidl

Top 5 Natural Language Processing Libraries for Data Scientist

In this blog post we are going to talk about Natural Language Processing (NLP) which is one of the branches of machine learning which focuses on teaching machines to understand human language. it has multiple applications, from chatbots to sentiment analysis, and is an important skill in the data scientist’s toolbox. let’s look at five…

Day 8: Text Classification with Naïve Bayes

Text Classification is a popular technique used in Natural Language Processing to categorize text documents into predefined categories. Naïve Bayes is a commonly used algorithm for text classification, as it is simple and efficient. In this blog post, we will look at the process of building a Text Classification model using Naïve Bayes, step-by-step. We…

Day 7: Building a Sentiment Analysis Model

In today’s world, where social media is the new norm, analyzing the sentiment behind the text has become important for businesses and organizations. Sentiment analysis refers to the process of determining the emotional tone behind a piece of text, whether it is positive, negative, or neutral. In this article, we will discuss how to build…

Day 6: Word Embeddings: an overview

Word embeddings are a powerful technique in natural language processing which can help us represent words in a more meaningful way than other approaches like one-hot encoding or bag of words. In this blog post, we’ll provide an overview of what word embeddings are, how they work, their advantages and limitations, popular models for generating…

Day 5: Part-of-Speech Tagging and Named Entity Recognition

Welcome back peeps as we have already discussed about the tokenization and stop words in our last article so, in this day 5 of Natural Language Processing (NLP) journey! In this blog we will be exploring two important techniques for analyzing text: Part-of-Speech (POS) tagging Named Entity Recognition (NER) 1 – Part-of-Speech (POS) tagging is…

Day 4: Stemming and Lemmatization

IntroductionNatural Language Processing (NLP) plays a critical role in understanding and processing human language. This blog discusses stemming and lemmatization, essential text normalization techniques in NLP. What is NLP and Its Components?NLP is an AI-based method of interacting with systems using natural language. It involves several steps: tokenization, lemmatization, POS tagging, named entity recognition, and…

Day 3: Tokenization and stopword removal

Tokenization and stop word removal are two important steps in pre-processing text data for natural language processing (NLP) tasks. These steps help to prepare the text data for further analysis, modelling, and modelling training. Tokenization is the process of breaking down a larger piece of text into smaller units, called tokens, which can then be…

Day 2: Pre-processing Text Data: Cleaning and Normalization

Pre-processing is an important step in any Natural Language Processing (NLP) project. It involves cleaning and normalizing the text data so that it can be processed effectively by NLP algorithms and models. The aim of pre-processing is to improve the quality of the data and make it easier for NLP algorithms to process. In this…

Day 1: 30 days of Natural Language Processing (NLP)

Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) that focuses on the interaction between computers and humans using natural language. It is a rapidly growing field that has revolutionized the way computers process, understand, and generate human language. In this blog, we will be exploring what NLP is, its history, and its…

Unleashing Emotions: Vader for Sentiment Analysis

VADER (Valence Aware Dictionary and Sentiment Reasoner) is a lexicon and rule-based sentiment analysis library that is specifically attuned to sentiments expressed in social media. It is used for sentiment analysis tasks, especially in social media and online reviews, where the language used can be informal and often contains slang, emoticons, and sarcasm. It uses…