Tag: nlp
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…
Read MoreDay 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…
Read MoreDay 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…
Read MoreDay 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…
Read MoreDay 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…
Read MoreDay 4: Stemming and Lemmatization
Stemming and lemmatization are two popular techniques for text pre-processing in natural language processing (NLP) tasks. In this article, we will discuss what stemming and lemmatization are and provide examples to illustrate their application. Stemming is the process of reducing a word to its root or stem form. For example, the stem of the word…
Read MoreDay 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…
Read MoreDay 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…
Read MoreDay 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…
Read MoreUnleashing 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…
Read More