Tools for Joint Sentiment and Topic Analysis of Textual Data
Conversions from other packages to LDA
Convert back a dfm to a tokens object
Distances between topic models (chains)
Compute scores of topic models (chains)
Coherence of estimated topics
Compute scores using the Picault-Renault lexicon
Estimate a topic model
Download press conferences from the European Central Bank
Download and pre-process speeches from the European Central Bank
Create a Joint Sentiment/Topic model
Create a Latent Dirichlet Allocation model
Visualize a LDA model using LDAvis
Replacement generic for data.table::melt()
Melt for sentopicmodels
Merge topics into fewer themes
Plot the distances between topic models (chains)
Plot a topic model using Plotly
Print method for sentopics models
Compute the topic or sentiment proportion time series
Objects exported from other packages
Re-initialize a topic model
Create a Reversed Joint Sentiment/Topic model
Breakdown the sentiment into topical components
Compute a sentiment time series
Compute time series of topical sentiments
Create a sentopic model
Internal date
Setting topic or sentiment labels
Internal sentiment
Internal conversions between sentopics models.
Tools for joining sentiment and topic analysis (sentopics)
Extract the most representative words from topics
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.
Useful links