WeatherSentiment: Comprehensive Analysis of Tweet Sentiments and Weather Data
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.
| Version: |
1.0 |
| Depends: |
R (≥ 4.1.0), tidyverse, wordcloud, sentimentr |
| Imports: |
tidytext, ggplot2, stringr, data.table, RColorBrewer, tidyr |
| Suggests: |
dplyr, syuzhet |
| Published: |
2024-08-19 |
| DOI: |
10.32614/CRAN.package.WeatherSentiment |
| Author: |
Andriette Bekker [aut],
Mohammad Arashi [aut],
Leila Marvian Mashhad [aut, cre],
Priyanka Nagar [aut] |
| Maintainer: |
Leila Marvian Mashhad <Leila.marveian at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| CRAN checks: |
WeatherSentiment results |
Documentation:
Downloads:
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