Showing 60 of total 60 results (show query)
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.
amplifierpolaritysentimentsentiment-analysisvalence-shifter
43.9 match 432 stars 9.43 score 680 scripts 2 dependents
sentometricsresearch
sentometrics:An Integrated Framework for Textual Sentiment Time Series Aggregation and Prediction
Optimized prediction based on textual sentiment, accounting for the intrinsic challenge that sentiment can be computed and pooled across texts and time in various ways. See Ardia et al. (2021) <doi:10.18637/jss.v099.i02>.
Maintained by Samuel Borms. Last updated 4 years ago.
nlppredictionsentiment-analysistext-miningtime-seriesopenblascppopenmp
61.1 match 83 stars 6.09 score 49 scripts
sfeuerriegel
SentimentAnalysis:Dictionary-Based Sentiment Analysis
Performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as Harvard IV, or finance-specific dictionaries. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.
Maintained by Nicolas Proellochs. Last updated 2 years ago.
33.2 match 149 stars 8.34 score 242 scripts 1 dependents
mjockers
syuzhet:Extracts Sentiment and Sentiment-Derived Plot Arcs from Text
Extracts sentiment and sentiment-derived plot arcs from text using a variety of sentiment dictionaries conveniently packaged for consumption by R users. Implemented dictionaries include "syuzhet" (default) developed in the Nebraska Literary Lab "afinn" developed by Finn Årup Nielsen, "bing" developed by Minqing Hu and Bing Liu, and "nrc" developed by Mohammad, Saif M. and Turney, Peter D. Applicable references are available in README.md and in the documentation for the "get_sentiment" function. The package also provides a hack for implementing Stanford's coreNLP sentiment parser. The package provides several methods for plot arc normalization.
Maintained by Matthew Jockers. Last updated 2 years ago.
16.5 match 336 stars 12.92 score 1.4k scripts 31 dependents
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.
61.5 match 2.70 score 7 scriptstrinker
lexicon:Lexicons for Text Analysis
A collection of lexical hash tables, dictionaries, and word lists.
Maintained by Tyler Rinker. Last updated 3 years ago.
hashlexiconlookupnames-frequentstopwordstext-dictionariestext-mining
17.1 match 111 stars 8.80 score 224 scripts 25 dependents
odelmarcelle
sentopics:Tools for Joint Sentiment and Topic Analysis of Textual Data
A framework that joins topic modeling and sentiment analysis of textual data. The package implements a fast Gibbs sampling estimation of Latent Dirichlet Allocation (Griffiths and Steyvers (2004) <doi:10.1073/pnas.0307752101>) and Joint Sentiment/Topic Model (Lin, He, Everson and Ruger (2012) <doi:10.1109/TKDE.2011.48>). It offers a variety of helpers and visualizations to analyze the result of topic modeling. The framework also allows enriching topic models with dates and externally computed sentiment measures. A flexible aggregation scheme enables the creation of time series of sentiment or topical proportions from the enriched topic models. Moreover, a novel method jointly aggregates topic proportions and sentiment measures to derive time series of topical sentiment.
Maintained by Olivier Delmarcelle. Last updated 2 months ago.
22.0 match 8 stars 5.38 score 5 scripts
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-lexiconlatent-dirichlet-allocationn-gramsplotsentiment-analysistidy-datatopicanalysistweetstwitter-data
17.2 match 12 stars 6.33 score 118 scripts
juliasilge
tidytext:Text Mining using 'dplyr', 'ggplot2', and Other Tidy Tools
Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like 'dplyr', 'broom', 'tidyr', and 'ggplot2'. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages.
Maintained by Julia Silge. Last updated 11 months ago.
natural-language-processingtext-miningtidy-datatidyverse
5.3 match 1.2k stars 16.86 score 17k scripts 61 dependents
ropensci
googleLanguageR:Call Google's 'Natural Language' API, 'Cloud Translation' API, 'Cloud Speech' API and 'Cloud Text-to-Speech' API
Call 'Google Cloud' machine learning APIs for text and speech tasks. Call the 'Cloud Translation' API <https://cloud.google.com/translate/> for detection and translation of text, the 'Natural Language' API <https://cloud.google.com/natural-language/> to analyse text for sentiment, entities or syntax, the 'Cloud Speech' API <https://cloud.google.com/speech/> to transcribe sound files to text and the 'Cloud Text-to-Speech' API <https://cloud.google.com/text-to-speech/> to turn text into sound files.
Maintained by Mark Edmondson. Last updated 8 months ago.
cloud-speech-apicloud-translation-apigoogle-api-clientgoogle-cloudgoogle-cloud-speechgoogle-nlpgoogleauthrnatural-language-processingpeer-reviewedsentiment-analysisspeech-apitranslation-api
8.0 match 196 stars 10.36 score 268 scripts 3 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.
sentimentsentiment-analysistidyversetwitter-apitwitter-sentiment-analysis
28.9 match 1 stars 2.70 score
koheiw
LSX:Semi-Supervised Algorithm for Document Scaling
A word embeddings-based semi-supervised model for document scaling Watanabe (2020) <doi:10.1080/19312458.2020.1832976>. LSS allows users to analyze large and complex corpora on arbitrary dimensions with seed words exploiting efficiency of word embeddings (SVD, Glove). It can generate word vectors on a users-provided corpus or incorporate a pre-trained word vectors.
Maintained by Kohei Watanabe. Last updated 2 months ago.
lsaquantedasentiment-analysistext-analysis
11.0 match 55 stars 6.09 score 14 scripts
chris31415926535
tardis:Text Analysis with Rules and Dictionaries for Inferring Sentiment
Measure text's sentiment with dictionaries and simple rules covering negations and modifiers. User-supplied dictionaries are supported, including Unicode emojis and multi-word tokens, so this package can also be used to study constructs beyond sentiment.
Maintained by Christopher Belanger. Last updated 2 years ago.
16.7 match 2 stars 4.00 score 10 scripts
quanteda
quanteda:Quantitative Analysis of Textual Data
A fast, flexible, and comprehensive framework for quantitative text analysis in R. Provides functionality for corpus management, creating and manipulating tokens and n-grams, exploring keywords in context, forming and manipulating sparse matrices of documents by features and feature co-occurrences, analyzing keywords, computing feature similarities and distances, applying content dictionaries, applying supervised and unsupervised machine learning, visually representing text and text analyses, and more.
Maintained by Kenneth Benoit. Last updated 2 months ago.
corpusnatural-language-processingquantedatext-analyticsonetbbcpp
3.8 match 851 stars 16.68 score 5.4k scripts 51 dependents
cran
zoomGroupStats:Analyze Text, Audio, and Video from 'Zoom' Meetings
Provides utilities for processing and analyzing the files that are exported from a recorded 'Zoom' Meeting. This includes analyzing data captured through video cameras and microphones, the text-based chat, and meta-data. You can analyze aspects of the conversation among meeting participants and their emotional expressions throughout the meeting.
Maintained by Andrew Knight. Last updated 4 years ago.
18.3 match 3.30 score 10 scripts
chaoliu-cl
conversim:Conversation Similarity Analysis Package
A comprehensive toolkit for analyzing and comparing conversations. This package provides functions to calculate various similarity measures between conversations, including topic, lexical, semantic, structural, stylistic, sentiment, participant, and timing similarities. It supports both pairwise conversation comparisons and analysis of multiple dyads.
Maintained by Chao Liu. Last updated 6 months ago.
11.8 match 4.30 score 10 scripts
atomashevic
transforEmotion:Sentiment Analysis for Text, Image and Video using Transformer Models
Implements sentiment analysis using huggingface <https://huggingface.co> transformer zero-shot classification model pipelines for text and image data. The default text pipeline is Cross-Encoder's DistilRoBERTa <https://huggingface.co/cross-encoder/nli-distilroberta-base> and default image/video pipeline is Open AI's CLIP <https://huggingface.co/openai/clip-vit-base-patch32>. All other zero-shot classification model pipelines can be implemented using their model name from <https://huggingface.co/models?pipeline_tag=zero-shot-classification>.
Maintained by Aleksandar Tomašević. Last updated 2 months ago.
7.3 match 26 stars 6.40 score 12 scripts
emilhvitfeldt
textdata:Download and Load Various Text Datasets
Provides a framework to download, parse, and store text datasets on the disk and load them when needed. Includes various sentiment lexicons and labeled text data sets for classification and analysis.
Maintained by Emil Hvitfeldt. Last updated 10 months ago.
4.4 match 75 stars 9.66 score 1.4k scripts 1 dependentschaoliu-cl
Goodreader:Scrape and Analyze 'Goodreads' Book Data
A comprehensive toolkit for scraping and analyzing book data from <https://www.goodreads.com/>. This package provides functions to search for books, scrape book details and reviews, perform sentiment analysis on reviews, and conduct topic modeling. It's designed for researchers, data analysts, and book enthusiasts who want to gain insights from 'Goodreads' data.
Maintained by Chao Liu. Last updated 13 days ago.
9.1 match 4.40 score 5 scripts
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.
9.0 match 4 stars 4.30 score 4 scriptstrinker
qdap:Bridging the Gap Between Qualitative Data and Quantitative Analysis
Automates many of the tasks associated with quantitative discourse analysis of transcripts containing discourse including frequency counts of sentence types, words, sentences, turns of talk, syllables and other assorted analysis tasks. The package provides parsing tools for preparing transcript data. Many functions enable the user to aggregate data by any number of grouping variables, providing analysis and seamless integration with other R packages that undertake higher level analysis and visualization of text. This affords the user a more efficient and targeted analysis. 'qdap' is designed for transcript analysis, however, many functions are applicable to other areas of Text Mining/ Natural Language Processing.
Maintained by Tyler Rinker. Last updated 4 years ago.
qdapquantitative-discourse-analysistext-analysistext-miningtext-plottingopenjdk
3.8 match 176 stars 9.61 score 1.3k scripts 3 dependentscran
RSentiment:Analyse Sentiment of English Sentences
Analyses sentiment of a sentence in English and assigns score to it. It can classify sentences to the following categories of sentiments:- Positive, Negative, very Positive, very negative, Neutral. For a vector of sentences, it counts the number of sentences in each category of sentiment.In calculating the score, negation and various degrees of adjectives are taken into consideration. It deals only with English sentences.
Maintained by Subhasree Bose. Last updated 7 years ago.
13.2 match 2.70 score
amalan-constat
SouthParkRshiny:Data and 'Shiny' Application for the Show 'SouthPark'
Ratings, votes, swear words and sentiments are analysed for the show 'SouthPark' through a 'Shiny' application after web scraping from 'IMDB' and the website <https://southpark.fandom.com/wiki/South_Park_Archives>.
Maintained by Amalan Mahendran. Last updated 1 years ago.
12.5 match 1 stars 2.70 score
c0reyes
TextMiningGUI:Text Mining GUI Interface
Graphic interface for text analysis, implement a few methods such as biplots, correspondence analysis, co-occurrence, clustering, topic models, correlations and sentiments.
Maintained by Conrado Reyes. Last updated 4 years ago.
analysisbiplotbiplotscorrelationssentimentstextminingtopic-models
10.5 match 3 stars 3.18 score 6 scripts
massimoaria
tall:Text Analysis for All
An R 'shiny' app designed for diverse text analysis tasks, offering a wide range of methodologies tailored to Natural Language Processing (NLP) needs. It is a versatile, general-purpose tool for analyzing textual data. 'tall' features a comprehensive workflow, including data cleaning, preprocessing, statistical analysis, and visualization, all integrated for effective text analysis.
Maintained by Massimo Aria. Last updated 3 days ago.
r-shinytext-analysis-and-sentiment-analysistext-classificationtext-miningtextual-analysiscpp
6.3 match 14 stars 5.12 score
serkor1
cryptoQuotes:Open Access to Cryptocurrency Market Data, Sentiment Indicators and Interactive Charts
This high-level API client provides open access to cryptocurrency market data, sentiment indicators, and interactive charting tools. The data is sourced from major cryptocurrency exchanges via 'curl' and returned in 'xts'-format. The data comes in open, high, low, and close (OHLC) format with flexible granularity, ranging from seconds to months. This flexibility makes it ideal for developing and backtesting trading strategies or conducting detailed market analysis.
Maintained by Serkan Korkmaz. Last updated 4 months ago.
binancebinance-apibitmartbybitbybit-apicryptocurrenciescryptocurrencycryptocurrency-exchangeshuobihuobi-apikraken-apikraken-exchange-apikucoinkucoin-api
4.8 match 39 stars 6.66 score 26 scripts
ramikrispin
TSstudio:Functions for Time Series Analysis and Forecasting
Provides a set of tools for descriptive and predictive analysis of time series data. That includes functions for interactive visualization of time series objects and as well utility functions for automation time series forecasting.
Maintained by Rami Krispin. Last updated 2 years ago.
forecastingtime-seriestimeseriestsstudiovisualization
3.4 match 425 stars 9.02 score 656 scripts
lucasgodeiro
TextForecast:Regression Analysis and Forecasting Using Textual Data from a Time-Varying Dictionary
Provides functionalities based on the paper "Time Varying Dictionary and the Predictive Power of FED Minutes" (Lima, 2018) <doi:10.2139/ssrn.3312483>. It selects the most predictive terms, that we call time-varying dictionary using supervised machine learning techniques as lasso and elastic net.
Maintained by Lucas Godeiro. Last updated 5 years ago.
6.0 match 15 stars 5.18 score 20 scripts
philferriere
mscstexta4r:R Client for the Microsoft Cognitive Services Text Analytics REST API
R Client for the Microsoft Cognitive Services Text Analytics REST API, including Sentiment Analysis, Topic Detection, Language Detection, and Key Phrase Extraction. An account MUST be registered at the Microsoft Cognitive Services website <https://www.microsoft.com/cognitive-services/> in order to obtain a (free) API key. Without an API key, this package will not work properly.
Maintained by Phil Ferriere. Last updated 9 years ago.
5.4 match 24 stars 5.28 score 16 scripts
bnosac
udpipe:Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the 'UDPipe' 'NLP' Toolkit
This natural language processing toolkit provides language-agnostic 'tokenization', 'parts of speech tagging', 'lemmatization' and 'dependency parsing' of raw text. Next to text parsing, the package also allows you to train annotation models based on data of 'treebanks' in 'CoNLL-U' format as provided at <https://universaldependencies.org/format.html>. The techniques are explained in detail in the paper: 'Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe', available at <doi:10.18653/v1/K17-3009>. The toolkit also contains functionalities for commonly used data manipulations on texts which are enriched with the output of the parser. Namely functionalities and algorithms for collocations, token co-occurrence, document term matrix handling, term frequency inverse document frequency calculations, information retrieval metrics (Okapi BM25), handling of multi-word expressions, keyword detection (Rapid Automatic Keyword Extraction, noun phrase extraction, syntactical patterns) sentiment scoring and semantic similarity analysis.
Maintained by Jan Wijffels. Last updated 2 years ago.
conlldependency-parserlemmatizationnatural-language-processingnlppos-taggingr-pkgrcpptext-miningtokenizerudpipecpp
2.2 match 215 stars 11.83 score 1.2k scripts 9 dependents
wrathematics
meanr:Sentiment Analysis Scorer
Sentiment analysis is a popular technique in text mining that attempts to determine the emotional state of some text. We provide a new implementation of a common method for computing sentiment, whereby words are scored as positive or negative according to a dictionary lookup. Then the sum of those scores is returned for the document. We use the 'Hu' and 'Liu' sentiment dictionary ('Hu' and 'Liu', 2004) <doi:10.1145/1014052.1014073> for determining sentiment. The scoring function is 'vectorized' by document, and scores for multiple documents are computed in parallel via 'OpenMP'.
Maintained by Drew Schmidt. Last updated 1 years ago.
6.2 match 22 stars 4.04 score 8 scripts
rstudio
keras3:R Interface to 'Keras'
Interface to 'Keras' <https://keras.io>, a high-level neural networks API. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both CPU and GPU devices.
Maintained by Tomasz Kalinowski. Last updated 4 days ago.
1.8 match 845 stars 13.57 score 264 scripts 2 dependents
zackfisher
MIIVsem:Model Implied Instrumental Variable (MIIV) Estimation of Structural Equation Models
Functions for estimating structural equation models using instrumental variables.
Maintained by Zachary Fisher. Last updated 1 years ago.
4.0 match 9 stars 5.19 score 29 scripts 2 dependentstrinker
qdapDictionaries:Dictionaries and Word Lists for the 'qdap' Package
A collection of text analysis dictionaries and word lists for use with the 'qdap' package.
Maintained by Tyler Rinker. Last updated 7 years ago.
3.4 match 4 stars 5.99 score 113 scripts 6 dependents
manalytics
opitools:Analyzing the Opinions in a Big Text Document
Designed for performing impact analysis of opinions in a digital text document (DTD). The package allows a user to assess the extent to which a theme or subject within a document impacts the overall opinion expressed in the document. The package can be applied to a wide range of opinion-based DTD, including commentaries on social media platforms (such as 'Facebook', 'Twitter' and 'Youtube'), online products reviews, and so on. The utility of 'opitools' was originally demonstrated in Adepeju and Jimoh (2021) <doi:10.31235/osf.io/c32qh> in the assessment of COVID-19 impacts on neighbourhood policing using Twitter data. Further examples can be found in the vignette of the package.
Maintained by Monsuru Adepeju. Last updated 2 years ago.
3.8 match 12 stars 5.30 score 11 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.
analyticsapiautomationautomldata-sciencedescriptive-statisticsh2omachine-learningmarketingmmmpredictive-modelingpuzzlerlanguagerobynvisualization
2.0 match 233 stars 9.84 score 185 scripts 1 dependents
tjarkvandemerwe
tidyprompt:Prompt Large Language Models and Enhance Their Functionality
Easily construct prompts and associated logic for interacting with large language models (LLMs). 'tidyprompt' introduces the concept of prompt wraps, which are building blocks that you can use to quickly turn a simple prompt into a complex one. Prompt wraps do not just modify the prompt text, but also add extraction and validation functions that will be applied to the response of the LLM. This ensures that the user gets the desired output. 'tidyprompt' can add various features to prompts and their evaluation by LLMs, such as structured output, automatic feedback, retries, reasoning modes, autonomous R function calling, and R code generation and evaluation. It is designed to be compatible with any LLM provider that offers chat completion.
Maintained by Luka Koning. Last updated 1 months ago.
3.0 match 16 stars 6.56 score 9 scripts
t-kalinowski
keras:R Interface to 'Keras'
Interface to 'Keras' <https://keras.io>, a high-level neural networks 'API'. 'Keras' was developed with a focus on enabling fast experimentation, supports both convolution based networks and recurrent networks (as well as combinations of the two), and runs seamlessly on both 'CPU' and 'GPU' devices.
Maintained by Tomasz Kalinowski. Last updated 11 months ago.
1.8 match 10.82 score 10k scripts 54 dependents
r-lib
available:Check if the Title of a Package is Available, Appropriate and Interesting
Check if a given package name is available to use. It checks the name's validity. Checks if it is used on 'GitHub', 'CRAN' and 'Bioconductor'. Checks for unintended meanings by querying 'Wiktionary' and Wikipedia.
Maintained by Gábor Csárdi. Last updated 7 months ago.
2.0 match 145 stars 8.25 score 119 scripts 4 dependents
gunratan
edgar:Tool for the U.S. SEC EDGAR Retrieval and Parsing of Corporate Filings
In the USA, companies file different forms with the U.S. Securities and Exchange Commission (SEC) through EDGAR (Electronic Data Gathering, Analysis, and Retrieval system). The EDGAR database automated system collects all the different necessary filings and makes it publicly available. This package facilitates retrieving, storing, searching, and parsing of all the available filings on the EDGAR server. It downloads filings from SEC server in bulk with a single query. Additionally, it provides various useful functions: extracts 8-K triggering events, extract "Business (Item 1)" and "Management's Discussion and Analysis(Item 7)" sections of annual statements, searches filings for desired keywords, provides sentiment measures, parses filing header information, and provides HTML view of SEC filings.
Maintained by Gunratan Lonare. Last updated 9 days ago.
5.9 match 10 stars 2.79 score 61 scripts
tidyverse
ellmer:Chat with Large Language Models
Chat with large language models from a range of providers including 'Claude' <https://claude.ai>, 'OpenAI' <https://chatgpt.com>, and more. Supports streaming, asynchronous calls, tool calling, and structured data extraction.
Maintained by Hadley Wickham. Last updated 1 days ago.
1.3 match 391 stars 12.65 score 98 scripts 7 dependents
mlverse
mall:Run Multiple Large Language Model Predictions Against a Table, or Vectors
Run multiple 'Large Language Model' predictions against a table. The predictions run row-wise over a specified column. It works using a one-shot prompt, along with the current row's content. The prompt that is used will depend of the type of analysis needed.
Maintained by Edgar Ruiz. Last updated 3 months ago.
data-sciencedplyrllmpolarspython
2.3 match 86 stars 6.61 score 94 scripts
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.
4.0 match 2 stars 3.00 score 4 scripts
gesistsa
sweater:Speedy Word Embedding Association Test and Extras Using R
Conduct various tests for evaluating implicit biases in word embeddings: Word Embedding Association Test (Caliskan et al., 2017), <doi:10.1126/science.aal4230>, Relative Norm Distance (Garg et al., 2018), <doi:10.1073/pnas.1720347115>, Mean Average Cosine Similarity (Mazini et al., 2019) <arXiv:1904.04047>, SemAxis (An et al., 2018) <arXiv:1806.05521>, Relative Negative Sentiment Bias (Sweeney & Najafian, 2019) <doi:10.18653/v1/P19-1162>, and Embedding Coherence Test (Dev & Phillips, 2019) <arXiv:1901.07656>.
Maintained by Chung-hong Chan. Last updated 1 months ago.
bias-detectiontextanalysiswordembeddingcpp
2.4 match 30 stars 4.80 score 14 scripts
brianweinstein
googlenlp:An Interface to Google's Cloud Natural Language API
Interact with Google's Cloud Natural Language API <https://cloud.google.com/natural-language/> (v1) via R. The API has four main features, all of which are available through this R package: syntax analysis and part-of-speech tagging, entity analysis, sentiment analysis, and language identification.
Maintained by Brian Weinstien. Last updated 7 years ago.
2.8 match 8 stars 3.86 score 18 scripts
cloudyr
aws.comprehend:Client for 'AWS Comprehend'
Client for 'AWS Comprehend' <https://aws.amazon.com/comprehend>, a cloud natural language processing service that can perform a number of quantitative text analyses, including language detection, sentiment analysis, and feature extraction.
Maintained by Antoine Sachet. Last updated 5 years ago.
2.4 match 12 stars 4.40 score 14 scriptsmlverse
torchdatasets:Ready to Use Extra Datasets for Torch
Provides datasets in a format that can be easily consumed by torch 'dataloaders'. Handles data downloading from multiple sources, caching and pre-processing so users can focus only on their model implementations.
Maintained by Daniel Falbel. Last updated 5 days ago.
1.8 match 15 stars 5.65 score 99 scripts
colinfay
proustr:Tools for Natural Language Processing in French
Tools for Natural Language Processing in French and texts from Marcel Proust's collection "A La Recherche Du Temps Perdu". The novels contained in this collection are "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".
Maintained by Colin Fay. Last updated 6 years ago.
1.6 match 24 stars 6.10 score 104 scripts
krgitcode
vader:Valence Aware Dictionary and sEntiment Reasoner (VADER)
A lexicon and rule-based sentiment analysis tool that is specifically attuned to sentiments expressed in social media, and works well on texts from other domains. Hutto & Gilbert (2014) <https://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8109/8122>.
Maintained by Katherine Roehrick. Last updated 5 years ago.
3.7 match 1 stars 2.55 score 117 scripts 1 dependents
cstarbuck
peopleanalytics:Data Sets for Craig Starbuck's Book, "The Fundamentals of People Analytics: With Applications in R"
Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".
Maintained by Craig Starbuck. Last updated 2 years ago.
6.6 match 1.23 score 17 scripts
vosonlab
VOSONDash:User Interface for Collecting and Analysing Social Networks
A 'Shiny' application for the interactive visualisation and analysis of networks that also provides a web interface for collecting social media data using 'vosonSML'.
Maintained by Bryan Gertzel. Last updated 2 years ago.
r-shinyrstudioshinysocial-network-analysisvoson
1.8 match 56 stars 4.45 score 6 scripts
taylor-arnold
coreNLP:Wrappers Around Stanford CoreNLP Tools
Provides a minimal interface for applying annotators from the 'Stanford CoreNLP' java library. Methods are provided for tasks such as tokenisation, part of speech tagging, lemmatisation, named entity recognition, coreference detection and sentiment analysis.
Maintained by Taylor Arnold. Last updated 3 years ago.
2.5 match 1 stars 3.04 score 55 scripts
frankiethull
kuzco:LLM image classification using ollama in R
This package is a designed to use local models for image classification. The prompts and functions are designed to take an input image and supply classification information as an output.
Maintained by Frank Hull. Last updated 2 months ago.
1.9 match 11 stars 3.34 scorecran
deFit:Fitting Differential Equations to Time Series Data
Use numerical optimization to fit ordinary differential equations (ODEs) to time series data to examine the dynamic relationships between variables or the characteristics of a dynamical system. It can now be used to estimate the parameters of ODEs up to second order, and can also apply to multilevel systems. See <https://github.com/yueqinhu/defit> for details.
Maintained by Yueqin Hu. Last updated 5 months ago.
3.6 match 1.00 score 2 scriptslucasgodeiro
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.
0.8 match 7 stars 4.54 score 4 scripts
shaptonstahl
GDELTtools:Download, Slice, and Normalize GDELT V1 Event and Sentiment API Data
The GDELT V1 Event data set is over 41 GB now and growing 250 MB a month. The number of source articles has increased over time and unevenly across countries. This package makes it easy to download a subset of that data, then normalize that data to facilitate valid time series analysis.
Maintained by Stephen R. Haptonstahl. Last updated 1 years ago.
2.8 match 1.18 score 15 scripts
sillasgonzaga
lexiconPT:Lexicons for Portuguese Text Analysis
Provides easy access for sentiment lexicons for those who want to do text analysis in Portuguese texts. As of now, two Portuguese lexicons are available: 'SentiLex-PT02' and 'OpLexicon' (v2.1 and v3.0).
Maintained by Sillas Gonzaga. Last updated 7 years ago.
0.5 match 57 stars 5.12 score 46 scripts
kumes
seasonalityPlot:Seasonality Variation Plots of Stock Prices and Cryptocurrencies
The price action at any given time is determined by investor sentiment and market conditions. Although there is no established principle, over a long period of time, things often move with a certain periodicity. This is sometimes referred to as anomaly. The seasonPlot() function in this package calculates and visualizes the average value of price movements over a year for any given period. In addition, the monthly increase or decrease in price movement is represented with a colored background. This seasonPlot() function can use the same symbols as the 'quantmod' package (e.g. ^IXIC, ^DJI, SPY, BTC-USD, and ETH-USD etc).
Maintained by Satoshi Kume. Last updated 6 months ago.
0.5 match 1 stars 3.00 score 6 scripts