updateRollingLDA: Updating an existing RollingLDA object
In rollinglda: Construct Consistent Time Series from Textual Data

View source: R/updateRollingLDA.R

updateRollingLDA

R Documentation

Updating an existing RollingLDA object

Description

Performs an update of an existing object consisting of a rolling version of Latent Dirichlet Allocation.

Usage

updateRollingLDA(
  x,
  texts,
  dates,
  chunks,
  memory,
  param = getParam(x),
  compute.topics = TRUE,
  memory.fallback = 0L,
  ...
)

## S3 method for class 'RollingLDA'
RollingLDA(
  x,
  texts,
  dates,
  chunks,
  memory,
  param = getParam(x),
  compute.topics = TRUE,
  memory.fallback = 0L,
  ...
)

Arguments

`x`	[`named list`] `RollingLDA` object.
`texts`	[`named list`] Tokenized texts.
`dates`	[`(un)named Date`] Sorted dates of the tokenized texts. If unnamed, it must match the order of texts.
`chunks`	[`Date` or `character(1)`] Sorted dates of the beginnings of each chunk to be modeled as updates. If passed as `character`, dates are determined by passing the minimum of `dates` as `from` argument, `max(dates)` as `to` argument and `chunks` as `by` argument in `seq.Date`. If not passed, all texts are interpreted as one chunk.
`memory`	[`Date`, `character(1)` or `integer(1)`] Dates of the beginnings of each chunk's memory. If passed as `character`, dates are determined by using the dates of the beginnings of each chunk and substracting the given time interval in `memory` passing it as `by` argument in `seq.Date`. If passed as `integer/numeric`, the dates are determined by going backwards the modeled texts chronologically and taking the date of the text at position `memory`.
`param`	[`named list`] with entries (Default is `getParam(x)`) `vocab.abs` [`integer(1)`] An absolute lower bound limit for which words are taken into account. All words are considered in the vocabularies that have a count higher than `vocab.abs` over all texts and at the same time a higher relative frequency than `vocab.rel`. `vocab.rel` [0,1] A relative lower bound limit for which words are taken into account. See also `vocab.abs`. `vocab.fallback` [`integer(1)`] An absolute lower bound limit for which words are taken into account. All words are considered in the vocabularies that have a count higher than `vocab.fallback` over all texts even if they might not have a higher relative frequency than `vocab.rel`. `doc.abs` [`integer(1)`] An absolute lower bound limit for which texts are taken into account. All texts are considered for modeling that have more words (subsetted to words occurring in the vocabularies) than `doc.abs`.
`compute.topics`	[`logical(1)`] Should the topic matrix of the LDA model be computed? Default is `TRUE`.
`memory.fallback`	[`integer(1)`] If there are no texts as memory in a certain chunk, `memory` is determined by going backwards the modeled texts chronologically and taking the date of the text at position `memory.fallback`. Default is 0, which means "end the fitting".
`...`	not implemented

Details

The function uses an existing RollingLDA object and models new texts with a specified memory as initialization of the new LDA chunk.

The function returns a RollingLDA object. You can receive results and all other elements of this object with getter functions (see getChunks).

Value

[named list] with entries

id: [character(1)] See above.
lda: LDA object of the fitted RollingLDA.
docs: [named list] with modeled texts in a preprocessed format. See LDAprep
dates: [named Date] with dates of the modeled texts.
vocab: [character] with the vocabularies considered for modeling.
chunks: [data.table] with specifications for each model chunk.
param: [named list] with parameter specifications for vocab.abs [integer(1)], vocab.rel [0,1], vocab.fallback [integer(1)] and doc.abs [integer(1)]. See above for explanation.

Examples


roll_lda = RollingLDA(texts = economy_texts[economy_dates < "2008-05-01"],
                      dates = economy_dates[economy_dates < "2008-05-01"],
                      chunks = "month",
                      memory = "month",
                      init = 100,
                      K = 10,
                      type = "lda")

# updateRollingLDA = RollingLDA, if first argument is a RollingLDA object
roll_update = RollingLDA(roll_lda,
                         texts = economy_texts[economy_dates >= "2008-05-01"],
                         dates = economy_dates[economy_dates >= "2008-05-01"],
                         chunks = "month",
                         memory = "month")

roll_update
getChunks(roll_update)

rollinglda documentation built on Oct. 1, 2022, 9:06 a.m.