# Tools for Joint Sentiment and Topic Analysis of Textual Data

## Documentation for package ‘sentopics’ version 1.0.0

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

sentopics-package | Tools for joining sentiment and topic analysis (sentopics)
as.LDA | Conversions from other packages to LDA
as.LDA.keyATM_output | Conversions from other packages to LDA
as.LDA.LDA_Gibbs | Conversions from other packages to LDA
as.LDA.LDA_VEM | Conversions from other packages to LDA
as.LDA.STM | Conversions from other packages to LDA
as.LDA.textmodel_lda | Conversions from other packages to LDA
as.LDA_lda | Conversions from other packages to LDA
as.tokens.dfm | Convert back a dfm to a tokens object
chains_distances | Distances between topic models (chains)
chains_scores | Compute scores of topic models (chains)
coherence | Coherence of estimated topics
compute_PicaultRenault_scores | Compute scores using the Picault-Renault lexicon
ECB_press_conferences | Corpus of press conferences from the European Central Bank
ECB_press_conferences_tokens | Tokenized press conferences
fit.JST | Estimate a topic model
fit.LDA | Estimate a topic model
fit.multi_chains | Estimate a topic model
fit.rJST | Estimate a topic model
fit.sentopicsmodel | Estimate a topic model
get_ECB_press_conferences | Download press conferences from the European Central Bank
get_ECB_speeches | Download and pre-process speeches from the European Central Bank
grow | Estimate a topic model
grow.JST | Estimate a topic model
grow.LDA | Estimate a topic model
grow.multi_chains | Estimate a topic model
grow.rJST | Estimate a topic model
grow.sentopicsmodel | Estimate a topic model
JST | Create a Joint Sentiment/Topic model
LDA | Create a Latent Dirichlet Allocation model
LDAvis | Visualize an LDA model using 'LDAvis'
LoughranMcDonald | Loughran-McDonald lexicon
melt.sentopicsmodel | Melt for sentopicsmodels
merge_topics | Merge topics into fewer themes
PicaultRenault | Picault-Renault lexicon
PicaultRenault_data | Regression dataset based on Picault & Renault (2017)
plot.multi_chains | Plot the distances between topic models (chains)
plot.sentopicsmodel | Plot a topic model using Plotly
plot_proportion_topics | Compute the topic or sentiment proportion time series
plot_sentiment_breakdown | Breakdown the sentiment into topical components
plot_sentiment_topics | Compute time series of topical sentiments
plot_top_words | Extract the most representative words from topics
print.JST | Print method for sentopics models
print.LDA | Print method for sentopics models
print.rJST | Print method for sentopics models
print.sentopicsmodel | Print method for sentopics models
proportion_topics | Compute the topic or sentiment proportion time series
reset | Re-initialize a topic model
rJST | Create a Reverse Joint Sentiment/Topic model
rJST.default | Create a Reverse Joint Sentiment/Topic model
rJST.LDA | Create a Reverse Joint Sentiment/Topic model
sentiment_breakdown | Breakdown the sentiment into topical components
sentiment_series | Compute a sentiment time series
sentiment_topics | Compute time series of topical sentiments
sentopics | Tools for joining sentiment and topic analysis (sentopics)
sentopics_date | Internal date
sentopics_date<- | Internal date
sentopics_labels | Setting topic or sentiment labels
sentopics_labels<- | Setting topic or sentiment labels
sentopics_sentiment | Internal sentiment
sentopics_sentiment<- | Internal sentiment
top_words | Extract the most representative words from topics