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WeatherSentiment
View on CRAN: Click
here
Download and install WeatherSentiment package within the R console
Install from CRAN:
install.packages("WeatherSentiment")
Install from Github:
library("remotes")
install_github("cran/WeatherSentiment")
Install by package version:
library("remotes")
install_version("WeatherSentiment", "1.0")
Attach the package and use:
library("WeatherSentiment")
Maintained by
Leila Marvian Mashhad
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
10.32614/CRAN.package.WeatherSentiment . WeatherSentiment results . WeatherSentiment.pdf . WeatherSentiment_1.0.tar.gz . WeatherSentiment_1.0.zip . WeatherSentiment_1.0.zip . WeatherSentiment_1.0.zip . WeatherSentiment_1.0.tgz . WeatherSentiment_1.0.tgz . WeatherSentiment_1.0.tgz . WeatherSentiment_1.0.tgz . https://CRAN.R-project.org/package=WeatherSentiment .
First Published: 2024-08-19
Latest Update: 2024-08-19
Description:
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.
How to cite:
Leila Marvian Mashhad (2024). WeatherSentiment: Comprehensive Analysis of Tweet Sentiments and Weather Data. R package version 1.0, https://cran.r-project.org/web/packages/WeatherSentiment. Accessed 29 Apr. 2025.
Previous versions and publish date:
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