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.

Package details

AuthorOlivier Delmarcelle [aut, cre] (<https://orcid.org/0000-0003-4347-070X>), Samuel Borms [ctb] (<https://orcid.org/0000-0001-9533-1870>), Chengua Lin [cph] (Original JST implementation), Yulan He [cph] (Original JST implementation), Jose Bernardo [cph] (Original JST implementation), David Robinson [cph] (Implementation of reorder_within()), Julia Silge [cph] (Implementation of reorder_within(), <https://orcid.org/0000-0002-3671-836X>)
MaintainerOlivier Delmarcelle <delmarcelle.olivier@gmail.com>
LicenseGPL (>= 3)
Version0.7.1
URL https://github.com/odelmarcelle/sentopics
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("sentopics")

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sentopics documentation built on May 18, 2022, 5:05 p.m.