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 |
chainsDistances |
Distances between topic models (chains) |
chainsScores |
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.multiChains |
Estimate a topic model |
fit.rJST |
Estimate a topic model |
fit.sentopicmodel |
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.multiChains |
Estimate a topic model |
grow.rJST |
Estimate a topic model |
grow.sentopicmodel |
Estimate a topic model |
JST |
Create a Joint Sentiment/Topic model |
LDA |
Create a Latent Dirichlet Allocation model |
LDAvis |
Visualize a LDA model using 'LDAvis' |
LoughranMcDonald |
Loughran-McDonald lexicon |
melt |
Replacement generic for 'data.table::melt()' |
melt.sentopicmodel |
Melt for sentopicmodels |
mergeTopics |
Merge topics into fewer themes |
PicaultRenault |
Picault-Renault lexicon |
PicaultRenault_data |
Regression dataset based on Picault & Renault (2017) |
plot.multiChains |
Plot the distances between topic models (chains) |
plot.sentopicmodel |
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_topWords |
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.sentopicmodel |
Print method for sentopics models |
proportion_topics |
Compute the topic or sentiment proportion time series |
reset |
Re-initialize a topic model |
rJST |
Create a Reversed Joint Sentiment/Topic model |
rJST.default |
Create a Reversed Joint Sentiment/Topic model |
rJST.LDA |
Create a Reversed 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 |
topWords |
Extract the most representative words from topics |