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.
|Author||Olivier 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>)|
|Maintainer||Olivier Delmarcelle <email@example.com>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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