Contents
What is the best number of topics for LDA?
To decide on a suitable number of topics, you can compare the goodness-of-fit of LDA models fit with varying numbers of topics. You can evaluate the goodness-of-fit of an LDA model by calculating the perplexity of a held-out set of documents. The perplexity indicates how well the model describes a set of documents.
How does LDA reduce dimensionality?
LDA reduces dimensionality from original number of feature to C — 1 features, where C is the number of classes. LDA basically projects the data in a new linear feature space, obviously the classifier will reach high accuracy if the data are linear separable.
What is the purpose of LDA?
LDA stands for Latent Dirichlet Allocation, and it is a type of topic modeling algorithm. The purpose of LDA is to learn the representation of a fixed number of topics, and given this number of topics learn the topic distribution that each document in a collection of documents has.
How to choose number of topics for LDA model?
Tokenize and preprocess the text data using the function preprocessText which is listed at the end of this example. Set aside 10% of the documents at random for validation. Create a bag-of-words model from the training documents. Remove the words that do not appear more than two times in total.
How to evaluate goodness of fit of LDA model?
You can evaluate the goodness-of-fit of an LDA model by calculating the perplexity of a held-out set of documents. The perplexity indicates how well the model describes a set of documents. A lower perplexity suggests a better fit. Load the example data.
How are LDA and NMF used in topic modeling?
LDA allows in ranking terms for a given topic in terms of both the frequency of the term under that topic as well as the term’s exclusivity to the topic, which accounts for the degree to which it appears in that particular topic to the exclusion of others.
How to use topic distributions in classification models?
Use Topic Distributions directly as feature vectors in supervised classification models (Logistic Regression, SVC, etc) and get F1-score. Use the same 2016 LDA model to get topic distributions from 2017 ( the LDA model did not see this data!) Run supervised classification models again on the 2017 vectors and see if this generalizes.