word_cloud_tweet

This function generates a word cloud visualization of the most frequent words in a tweet after basic cleaning. Stop words and words less than 3 characters are removed.

A comprehensive suite of functions for processing,
analyzing, and visualizing textual data from tweets is offered.
Users can clean tweets, analyze their sentiments, visualize data,
and examine the correlation between sentiments and environmental
data such as weather conditions. Main features include text processing,
sentiment analysis, data visualization, correlation analysis, and
synthetic data generation. Text processing involves cleaning and preparing
tweets by removing textual noise and irrelevant words. Sentiment analysis
extracts and accurately analyzes sentiments from tweet texts using advanced
algorithms. Data visualization creates various charts like word clouds
and sentiment polarity graphs for visual representation of data. Correlation
analysis examines and calculates the correlation between tweet sentiments
and environmental variables such as weather conditions. Additionally,
random tweets can be generated for testing and evaluating the performance
of analyses, empowering users to effectively analyze and interpret 'Twitter'
data for research and commercial purposes.

Leila Marvian Mashhad

WeatherSentiment

Comprehensive Analysis of Tweet Sentiments and Weather Data

 Andriette Bekker

Mohammad Arashi

Priyanka Nagar

word_cloud_tweet function

<dl> <dt>tweet</dt>
<dd>A character string containing the text of the tweet.</dd></dl>

Arguments

Leila Marvian Mashhad and Andriette Bekker and Mohammad Arashi and Priyanka Nagar.

Author

Generate Word Cloud from Tweet Text — word_cloud_tweet

<dl>

 <dt>tweet</dt>
<dd>A character string containing the text of the tweet.</dd>

</dl>

word_cloud_tweet: Generate Word Cloud from Tweet Text

Description

Usage

Value

Arguments

Author

Examples