A Simple Approach to Twitter Sentiment Analysis in R Programming Language
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Updated
Jul 24, 2019 - R
A Simple Approach to Twitter Sentiment Analysis in R Programming Language
Front-end implementation code for Twitter Sentiment Analysis using R Shiny
Scripts in R to publical use
Sentiment Analysis on the tweets downloaded from Twitter
The project concerns an international e-commerce company* based in the USA who want to discover key insights from their customer database. They want to use some of the most advanced machine learning techniques to study their customers.
Sentiment Analysis using @RealDonaldTrump tweets using RStduio and Tableau
This project uses Lexicon-based approach for sentimental analysis of 1000 recent tweets of 4 countries. A sentiment score for each tweet is computed to ascertain the overall nature of the tweet.
a twitterbot that uses sentiment analysis to interact with and affirm certain points of view
This project was made in R just to know the actual reason behind knowing why people are using python over R. We have implemented naive bayes algorithm in R programming. Give a ⭐ if this project have helped you.
Shiny Dashboard for Twitter Analytics
A text mining project that analyzes the sentiments of the people behind the tweets of the top 4 most popular EPL Teams using the NRC Emotion Lexicon
An analysis of Sentiments on Election Day in Karnataka State Elections (India) 2018 using R.
Data collection and visualization of #NCA17 tweets
Machine learning implemented for twitter sentiment analysis with R.
A Shiny web application that uses tweets for sentiment and frequency analysis.
Explore your Twitter activity with R: Sentiment Analysis and Data Visualization. How to analyze your Twitter account (or any account), discover your habits and sentiments with the "rtweet" package and NLP.
Get tweet's sentiment analysis with R
Analysis of tweets using R
US Election, in particular, analysis of Biden and Trump's use of Twitter
This research wants to build a time serie of the polarity of tweets related to a cluster of firms, and compare it to the time serie of the same firms in the stock market.
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