R-universe search: tweets

cjbarrie

academictwitteR:Access the Twitter Academic Research Product Track V2 API Endpoint

Package to query the Twitter Academic Research Product Track, providing access to full-archive search and other v2 API endpoints. Functions are written with academic research in mind. They provide flexibility in how the user wishes to store collected data, and encourage regular storage of data to mitigate loss when collecting large volumes of tweets. They also provide workarounds to manage and reshape the format in which data is provided on the client side.

Maintained by Christopher Barrie. Last updated 2 years ago.

twitter twitter-api

18.1 match 275 stars 8.94 score 177 scripts

trinker

sentimentr:Calculate Text Polarity Sentiment

Calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).

Maintained by Tyler Rinker. Last updated 3 years ago.

amplifier polarity sentiment sentiment-analysis valence-shifter

15.6 match 432 stars 9.43 score 680 scripts 2 dependents

evan-l-munson

saotd:Sentiment Analysis of Twitter Data

This analytic is an in initial foray into sentiment analysis. This analytic will allow a user to access the Twitter API (once they create their own developer account), ingest tweets of their interest, clean / tidy data, perform topic modeling if interested, compute sentiment scores utilizing the Bing Lexicon, and output visualizations.

Maintained by Evan Munson. Last updated 7 months ago.

bing-lexicon latent-dirichlet-allocation n-grams plot sentiment-analysis tidy-data topicanalysis tweets twitter-data

14.8 match 12 stars 6.33 score 118 scripts

geoffjentry

twitteR:R Based Twitter Client

Provides an interface to the Twitter web API.

Maintained by Jeff Gentry. Last updated 9 years ago.

8.7 match 254 stars 10.18 score 2.0k scripts 1 dependents

abuchmueller

Twitmo:Twitter Topic Modeling and Visualization for R

Tailored for topic modeling with tweets and fit for visualization tasks in R. Collect, pre-process and analyze the contents of tweets using LDA and structural topic models (STM). Comes with visualizing capabilities like tweet and hashtag maps and built-in support for 'LDAvis'.

Maintained by Andreas Buchmueller. Last updated 3 years ago.

ctm geospatial lda nlp stm topic-modeling twitter twitter-api

14.2 match 20 stars 4.69 score 49 scripts

gadenbuie

tweetrmd:Embed Tweets in R Markdown

Easily embed Tweets anywhere R Markdown turns plain text into HTML.

Maintained by Garrick Aden-Buie. Last updated 2 years ago.

12.1 match 102 stars 5.04 score 215 scripts

statnmap

tweetrbot:Functions for a Twitter bot

Functions to allow retrieve, store and retweet regularly.

Maintained by Sebastien Rochette. Last updated 2 years ago.

15.4 match 3 stars 3.48 score 4 scripts

rudeboybert

fivethirtyeight:Data and Code Behind the Stories and Interactives at 'FiveThirtyEight'

Datasets and code published by the data journalism website 'FiveThirtyEight' available at <https://github.com/fivethirtyeight/data>. Note that while we received guidance from editors at 'FiveThirtyEight', this package is not officially published by 'FiveThirtyEight'.

Maintained by Albert Y. Kim. Last updated 2 years ago.

data-science datajournalism fivethirtyeight statistics

3.6 match 453 stars 10.98 score 1.7k scripts

pablobarbera

streamR:Access to Twitter Streaming API via R

Functions to access Twitter's filter, sample, and user streams, and to parse the output into data frames.

Maintained by Pablo Barbera. Last updated 6 years ago.

7.1 match 5.00 score 343 scripts 6 dependents

joeornstein

promptr:Format and Complete Few-Shot LLM Prompts

Format and submit few-shot prompts to OpenAI's Large Language Models (LLMs). Designed to be particularly useful for text classification problems in the social sciences. Methods are described in Ornstein, Blasingame, and Truscott (2024) <https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.

Maintained by Joe Ornstein. Last updated 2 months ago.

7.0 match 14 stars 4.54 score 2 scripts

vosonlab

voson.tcn:Twitter Conversation Networks and Analysis

Collects tweets and metadata for threaded conversations and generates networks.

Maintained by Bryan Gertzel. Last updated 1 years ago.

conversations sna twitter voson x

8.4 match 8 stars 3.64 score 11 scripts

gesistsa

oolong:Create Validation Tests for Automated Content Analysis

Intended to create standard human-in-the-loop validity tests for typical automated content analysis such as topic modeling and dictionary-based methods. This package offers a standard workflow with functions to prepare, administer and evaluate a human-in-the-loop validity test. This package provides functions for validating topic models using word intrusion, topic intrusion (Chang et al. 2009, <https://papers.nips.cc/paper/3700-reading-tea-leaves-how-humans-interpret-topic-models>) and word set intrusion (Ying et al. 2021) <doi:10.1017/pan.2021.33> tests. This package also provides functions for generating gold-standard data which are useful for validating dictionary-based methods. The default settings of all generated tests match those suggested in Chang et al. (2009) and Song et al. (2020) <doi:10.1080/10584609.2020.1723752>.

Maintained by Chung-hong Chan. Last updated 20 days ago.

textanalysis topicmodeling validation

4.0 match 54 stars 7.57 score 23 scripts

wurli

tweetcheck:Parse and Validate Tweet Text

An interface to 'twitter-text', a 'JavaScript' library which is responsible for determining the length/validity of a tweet and identifying/linking any URLs or special tags (e.g. mentions or hashtags) which may be present.

Maintained by Jacob Scott. Last updated 2 years ago.

9.9 match 1 stars 2.70 score 3 scripts

nicolarighetti

CooRTweet:Coordinated Networks Detection on Social Media

Detects a variety of coordinated actions on social media and outputs the network of coordinated users along with related information.

Maintained by Nicola Righetti. Last updated 2 days ago.

3.6 match 41 stars 6.96 score 11 scripts

gesistsa

grafzahl:Supervised Machine Learning for Textual Data Using Transformers and 'Quanteda'

Duct tape the 'quanteda' ecosystem (Benoit et al., 2018) <doi:10.21105/joss.00774> to modern Transformer-based text classification models (Wolf et al., 2020) <doi:10.18653/v1/2020.emnlp-demos.6>, in order to facilitate supervised machine learning for textual data. This package mimics the behaviors of 'quanteda.textmodels' and provides a function to setup the 'Python' environment to use the pretrained models from 'Hugging Face' <https://huggingface.co/>. More information: <doi:10.5117/CCR2023.1.003.CHAN>.

Maintained by Chung-hong Chan. Last updated 26 days ago.

3.8 match 41 stars 5.91 score 3 scripts

laresbernardo

lares:Analytics & Machine Learning Sidekick

Auxiliary package for better/faster analytics, visualization, data mining, and machine learning tasks. With a wide variety of family functions, like Machine Learning, Data Wrangling, Marketing Mix Modeling (Robyn), Exploratory, API, and Scrapper, it helps the analyst or data scientist to get quick and robust results, without the need of repetitive coding or advanced R programming skills.

Maintained by Bernardo Lares. Last updated 24 days ago.

analytics api automation automl data-science descriptive-statistics h2o machine-learning marketing mmm predictive-modeling puzzle rlanguage robyn visualization

2.3 match 233 stars 9.84 score 185 scripts 1 dependents

hakkisabah

tsentiment:Fetching Tweet Data for Sentiment Analysis

Which uses Twitter APIs for the necessary data in sentiment analysis, acts as a middleware with the approved Twitter Application. A special access key is given to users who subscribe to the application with their Twitter account. With this special access key, the user defined keyword for sentiment analysis can be searched in twitter recent searches and results can be obtained( more information <https://github.com/hakkisabah/tsentiment> ). In addition, a service named tsentiment-services has been developed to provide all these operations ( for more information <https://github.com/hakkisabah/tsentiment-services> ). After the successful results obtained and in line with the permissions given by the user, the results of the analysis of the word cloud and bar graph saved in the user folder directory can be seen. In each analysis performed, the previous analysis visual result is deleted and this is the basic information you need to know as a practice rule. 'tsentiment' package provides a free service that acts as a middleware for easy data extraction from Twitter, and in return, the user rate limit is reduced by 30 requests from the total limit and the remaining requests are used. These 30 requests are reserved for use in application analytics. For information about endpoints, you can refer to the limit information in the "GET search/tweets" row in the Endpoints column in the list at <https://developer.twitter.com/en/docs/twitter-api/v1/rate-limits>.

Maintained by Hakki Sabah. Last updated 2 years ago.

sentiment sentiment-analysis tidyverse twitter-api twitter-sentiment-analysis

8.0 match 1 stars 2.70 score

cmjt

stelfi:Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder

Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using 'TMB' (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the field(s).

Maintained by Charlotte M. Jones-Todd. Last updated 5 months ago.

cpp openmp

3.5 match 8 stars 5.90 score 4 scripts

cran

dslabs:Data Science Labs

Datasets and functions that can be used for data analysis practice, homework and projects in data science courses and workshops. 26 datasets are available for case studies in data visualization, statistical inference, modeling, linear regression, data wrangling and machine learning.

Maintained by Rafael A. Irizarry. Last updated 1 years ago.

5.6 match 3.56 score 2 dependents

zahiernasrudin

malaytextr:Text Mining for Bahasa Malaysia

It is designed to work with text written in Bahasa Malaysia. We provide functions and data sets that will make working with Bahasa Malaysia text much easier. For word stemming in particular, we will look up the Malay words in a dictionary and then proceed to remove "extra suffix" as explained in Khan, Rehman Ullah, Fitri Suraya Mohamad, Muh Inam UlHaq, Shahren Ahmad Zadi Adruce, Philip Nuli Anding, Sajjad Nawaz Khan, and Abdulrazak Yahya Saleh Al-Hababi (2017) <https://ijrest.net/vol-4-issue-12.html> . This package includes a dictionary of Malay words that may be used to perform word stemming, a dataset of Malay stop words, a dataset of sentiment words and a dataset of normalized words.

Maintained by Zahier Nasrudin. Last updated 2 years ago.

3.4 match 4 stars 4.30 score 4 scripts

pursuitofdatascience

tidyEmoji:Discovers Emoji from Text

Unicodes are not friendly to work with, and not all Unicodes are Emoji per se, making obtaining Emoji statistics a difficult task. This tool can help your experience of working with Emoji as smooth as possible, as it has the 'tidyverse' style.

Maintained by Youzhi Yu. Last updated 2 years ago.

3.6 match 2 stars 4.00 score 7 scripts

lucasgodeiro

TwitterAutomatedTrading:Automated Trading Using Tweets

Provides an integration to the 'metatrader 5'. The functionalities carry out automated trading using sentiment indexes computed from 'twitter' and/or 'stockwits'. The sentiment indexes are based on the ph.d. dissertation "Essays on Economic Forecasting Models" (Godeiro,2018) <https://repositorio.ufpb.br/jspui/handle/123456789/15198> The integration between the 'R' and the 'metatrader 5' allows sending buy/sell orders to the brokerage.

Maintained by Lucas Godeiro. Last updated 2 years ago.

3.1 match 7 stars 4.54 score 4 scripts

leilamarvian

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.

Maintained by Leila Marvian Mashhad. Last updated 7 months ago.

13.5 match 1.00 score

kwb-r

kwb.twitter:Simplify Access to Twitter Messages

Simplify access to Twitter messages.

Maintained by Hauke Sonnenberg. Last updated 3 years ago.

knowledge-repo project-fakin publication social-network twitter

5.0 match 2.70 score

yonicd

carbonate:Interact with 'carbon.js'

Create beautiful images of source code using 'carbon.js'<https://carbon.now.sh/about>.

Maintained by Jonathan Sidi. Last updated 3 years ago.

1.8 match 212 stars 6.83 score 32 scripts

cmu-delphi

epidatr:Client for Delphi's 'Epidata' API

The Delphi 'Epidata' API provides real-time access to epidemiological surveillance data for influenza, 'COVID-19', and other diseases for the USA at various geographical resolutions, both from official government sources such as the Center for Disease Control (CDC) and Google Trends and private partners such as Facebook and Change 'Healthcare'. It is built and maintained by the Carnegie Mellon University Delphi research group. To cite this API: David C. Farrow, Logan C. Brooks, Aaron 'Rumack', Ryan J. 'Tibshirani', 'Roni' 'Rosenfeld' (2015). Delphi 'Epidata' API. <https://github.com/cmu-delphi/delphi-epidata>.

Maintained by David Weber. Last updated 4 months ago.

1.8 match 5 stars 6.14 score 114 scripts

benwiseman

sentiment.ai:Simple Sentiment Analysis Using Deep Learning

Sentiment Analysis via deep learning and gradient boosting models with a lot of the underlying hassle taken care of to make the process as simple as possible. In addition to out-performing traditional, lexicon-based sentiment analysis (see <https://benwiseman.github.io/sentiment.ai/#Benchmarks>), it also allows the user to create embedding vectors for text which can be used in other analyses. GPU acceleration is supported on Windows and Linux.

Maintained by Ben Wiseman. Last updated 3 years ago.

4.0 match 2.70 score 7 scripts

cran

needmining:A Simple Needmining Implementation

Showcasing needmining (the semi-automatic extraction of customer needs from social media data) with Twitter data. It uses the handling of the Twitter API provided by the package 'rtweet' and the textmining algorithms provided by the package 'tm'. Niklas Kuehl (2016) <doi:10.1007/978-3-319-32689-4_14> wrote an introduction to the topic of needmining.

Maintained by Dorian Proksch. Last updated 6 years ago.

9.4 match 1.00 score 7 scripts

smbartell

MapGAM:Mapping Smoothed Effect Estimates from Individual-Level Data

Contains functions for mapping odds ratios, hazard ratios, or other effect estimates using individual-level data such as case-control study data, using generalized additive models (GAMs) or Cox models for smoothing with a two-dimensional predictor (e.g., geolocation or exposure to chemical mixtures) while adjusting linearly for confounding variables, using methods described by Kelsall and Diggle (1998), Webster at al. (2006), and Bai et al. (2020). Includes convenient functions for mapping point estimates and confidence intervals, efficient control sampling, and permutation tests for the null hypothesis that the two-dimensional predictor is not associated with the outcome variable (adjusting for confounders).

Maintained by Scott Bartell. Last updated 2 years ago.

3.8 match 1 stars 2.37 score 26 scripts 1 dependents

lazappi

clamour:Website for Twitter analysis results

clamour provides a template for setting up a website to display the results of analysis of Twitter hashtags.

Maintained by Luke Zappia. Last updated 5 years ago.

analysis twitter

2.3 match 5 stars 3.40 score 2 scripts

danielkovtun

rpredictit:Interface to the 'PredictIt' API

Wrapper to retrieve market data, explore available markets, and plot historical price data from the 'PredictIt' public API (<https://www.predictit.org/api/marketdata/all/>). The package comes with a demo 'shiny' application for illustrating example use cases. License to use data made available via the API is for non-commercial use and 'PredictIt' is the sole source of such data.

Maintained by Daniel Kovtun. Last updated 3 years ago.

1.8 match 4 stars 4.30 score 8 scripts

matt-dray

blogsnip:Misc RStudio Addins to Help R Markdown

Assorted RStudio addins for personal use that insert code snippets to help me write R Markdown documents, particularly {blogdown} posts.

Maintained by Matt Dray. Last updated 3 years ago.

blogdown rstudio rstudio-addin

2.3 match 3 stars 3.18 score

uribo

suryulib:Shinya Uryu's Personal R Packages

More about what it does (maybe more than one line) Use four spaces when indenting paragraphs within the Description.

Maintained by Shinya Uryu. Last updated 2 years ago.

3.5 match 1.70 score

simmieyungie

texter:An Easy Text and Sentiment Analysis Library

Implement text and sentiment analysis with 'texter'. Generate sentiment scores on text data and also visualize sentiments. 'texter' allows you to quickly generate insights on your data. It includes support for lexicons such as 'NRC' and 'Bing'.

Maintained by Simi Kafaru. Last updated 3 years ago.

1.8 match 2 stars 3.00 score 4 scripts

eu-ecdc

epitweetr:Early Detection of Public Health Threats from 'Twitter' Data

It allows you to automatically monitor trends of tweets by time, place and topic aiming at detecting public health threats early through the detection of signals (e.g. an unusual increase in the number of tweets). It was designed to focus on infectious diseases, and it can be extended to all hazards or other fields of study by modifying the topics and keywords. More information is available in the 'epitweetr' peer-review publication (doi:10.2807/1560-7917.ES.2022.27.39.2200177).

Maintained by Laura Espinosa. Last updated 1 years ago.

early-warning-systems epidemic-surveillance lucene machine-learning signal-detection spark twitter

0.8 match 56 stars 5.98 score 86 scripts

johncoene

graphTweets:Visualise Twitter Interactions

Allows building an edge table from data frame of tweets, also provides function to build nodes and another create a temporal graph.

Maintained by John Coene. Last updated 5 years ago.

network-analysis twitter

0.5 match 46 stars 5.49 score 67 scripts