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.