jst: Run a Joint Sentiment Topic model
In rJST: Joint Sentiment Topic Modelling

Description Usage Arguments Details Value Examples

Estimates a joint sentiment topic model using a Gibbs sampler, see Details for model description.

jst(
  dfm,
  sentiLexInput = NULL,
  numSentiLabs = 3,
  numTopics = 10,
  numIters = 3,
  updateParaStep = -1,
  alpha = -1,
  beta = -1,
  gamma = -1,
  excludeNeutral = FALSE
)

`dfm`	A quanteda dfm object
`sentiLexInput`	Optional: A quanteda dictionary object for semi-supervised learning. If a dictionary is used, `numSentiLabs` will be overridden by the number of categories in the dictionary object. An extra category will by default be added for neutral words. This can be turned off by setting `excludeNeutral = TRUE`.
`numSentiLabs`	Integer, the number of sentiment labels (defaults to 3)
`numTopics`	Integer, the number of topics (defaults to 10)
`numIters`	Integer, the number of iterations (defaults to 3 for test runs, optimize by hand)
`updateParaStep`	Integer. The number of iterations between optimizations of hyperparameter alpha
`alpha`	Double, hyperparameter for (defaults to .05 * (average docsize/number of sentitopics))
`beta`	Double, hyperparameter for (defaults to .01, with multiplier .9/.1 for sentiment dictionary presence)
`gamma`	Double, hyperparameter for (defaults to .05 * (average docsize/number of sentiment categories))
`excludeNeutral`	Boolean. If a dictionary is used, an extra category is added for neutral words. Words in the dictionary receive a low probability of being allocated there. If this is set to `TRUE`, the neutral sentiment category will be omitted. The variable is irrelevant if no dictionary is used. Defaults to `FALSE`.

Basic model description:

Lin, C. and He, Y., 2009, November. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and knowledge management (pp. 375-384). ACM.

Weak supervision adopted from:

Lin, C., He, Y., Everson, R. and Ruger, S., 2012. Weakly supervised joint sentiment-topic detection from text. IEEE Transactions on Knowledge and Data engineering, 24(6), pp.1134-1145.

A JST.result object containing a data.frame for each estimated parameter

model <- jst(quanteda::dfm(quanteda::data_corpus_irishbudget2010),
             paradigm(),
             numTopics = 5,
             numIters = 15) # Use more in practice!