Description Usage Arguments Details Value Examples
Estimates a joint sentiment topic model using a Gibbs sampler, see Details for model description.
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dfm |
A quanteda dfm object |
sentiLexInput |
Optional: A quanteda dictionary object for semi-supervised learning. If
a dictionary is used, |
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 |
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
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