In this Sentiment Analysis tutorial, You’ll learn how to use your custom lexicon (for any language other than English) or keywords dictionary to perform simple (slightly naive) sentiment analysis using R’s tidytext package. Note: This isn’t going to provide you the same accuracy as using the language model, but it’s going to get you to the fastest solution (with some accuracy tradeoff). This example deals with Turkish Sentiment Analysis Script.
This R programming tutorial teaches you how to deploy your R Shiny App for Free on Shinyapps.io (which offers a Free Tier). Usually when you develop Shiny Apps or Shiny Dashboards, You’ve a lot of options to deploy on servers like DigitalOcean, AWS and other Linux servers. Here we’re presenting a simple solution to deploy your R shiny web app so that the entire world (on the Internet) can use your App.
Google Colab is a Free (to certain limit) Hosted Notebook provided by Google. It comes with GPU and TPU RunTime. It’s been avaialble only for Python users. But recently, there’s was a tip shared which enables using R on Google Colab natively. Also, the R environment comes with a lot of R pakcages (including tidyverse) pre-installed. Video Walkthrough Launch Code Launch your Google Colab with R here - https://colab.
If there’s a dataset that’s been most used by data scientists / data analysts while they’re learning something or coaching something - it’s either iris (more R users) or titanic (more Python users). iris dataset isn’t most used just because it’s easy accessible but it’s something that you can use to demonstrate many data science concepts like correlation, regression, classification. The objective of this post is to introduce you to penguins dataset and get you started with a few code snippets so that you can take off yourself!