This article is mainly for R Programming lovers those who want to learn data science with R programming language. This topic is written about the potential R packages for natural language processing.
It is no longer difficult to understand what people think about a topic by analysing the tweets shared by people. Sentiment analysis is one of the most popular use cases for NLP (Natural Language Processing).
A Guide: Text Analysis, Text Analytics & Text Mining. A guide to what it is, applications & use cases, tools, and how it improves business decision-making
Helping communication practitioners get actionable insights through open data sources like Twitter. This article will focus on how can we use Twitter through R programming to extract valuable insights and communicate these findings to the relevant stakeholders using Tableau.
Using text mining techniques to analyze Trump, Biden’s speech. In my latest Youtube video, I used text mining techniques to develop the ultimate data-driven drinking game rules for the upcoming Presidential debates.
CORD Crusher: Slicing the COVID-19 Data into Summaries. My first deep dive into text data using natural language processing.
In this article we have used 30 videos from the two politicians, relatively uniformly spread from January 2020 until today (September 2020), to demonstrate how speech analytics can be used to extract valuable conclusions from such data.
How to get the Letter Frequency of the Documents and how to compare statistically the Letter Frequency Distributions. We will compare our observed relative frequencies with the letter frequency of the English language.
A Step-by-Step Tutorial for Conducting Sentiment Analysis - This article is the first part of the tutorial that introduces the specific techniques used to conduct sentiment analysis with Python.
Regular expressions or regex are a sequence of characters used to check whether a pattern exists in each text (string) or not, for example, to find out if “123” exists in “Practical123DataScie”.
Analysing the hidden trends and the useful insights behind any group chat in Python. Human beings have always been a social species that relies on cooperation to survive and thrive.
In this article, my colleagues and I used a dataset of text messages to build a prediction model to classify which texts are spam.
Genlte itnro to the nltk pakcage.This article will guide you through the creation of a simple auto-correct program in python. This project is to create two different spelling recommenders, that will be able to take in a user’s input and recommend a correctly spelled word. Very cool! Note: Test inputs: [‘cormulent’, ‘incendenece’, ‘validrate’].
The coronavirus pandemic of 2019 and 2020 and the civil rights crisis of 2020, led by the Black Lives Matter movement, have highlighted some of the major limitations of our society today.
This is the first blog in a series of posts where I try to talk about the changes in modeling techniques for Natural Language Processing tasks over the past few years.
Injust 17 weeks, nearly 51 million Americans have filed for unemployment insurance — that’s more than the number of claims filed during the Great Recession [business insider report].
This is a python package that helps you to extract the basic features from the text data such as hashtags, stopwords, numerics which will help you to understand the data.
Text Information Access Modes: In this article, I will be discussing various text information access modes (Push vs Pull & Querying vs Browsing)
A baseline model with LSTMs. The question remains open: how to learn semantics? what is semantics? would DL-based models be capable to learn semantics?
In this article, we are going to do text classification on IMDB data-set using Convolutional Neural Networks(CNN). We will go through the basics of Convolutional Neural Networks and how it can be used with text for classification.