Tools for Joint Sentiment and Topic Analysis of Textual Data

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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