Computes fuzzy-c-means-style membership of every document in every topic
from cosine distances to the stored centroids, without changing the
deterministic hard clustering. In high-dimensional embedding spaces
distances concentrate, so the textbook fuzzifier sharpness = 2 collapses
memberships toward the uniform 1/k; the default sharpness = 1.15
preserves contrast. The membership ranking of topics within a document is
invariant to sharpness; the probability magnitudes are not, and should be
interpreted relative to the chosen value.
Arguments
- object
A fitted
topics()model.- embeddings
Optional numeric matrix of document embeddings. When omitted, the embeddings stored by
topics(keep_embeddings = TRUE)are used.- sharpness
Fuzzy-c-means fuzzifier
m. Must be greater than 1; values close to 1 give crisper memberships.
Value
A base data frame with one row per document-topic pair and columns
document_id, topic, probability, and rank (1 is the strongest
topic of the document). Probabilities sum to 1 within each document.
References
Bezdek, J. C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York. doi:10.1007/978-1-4757-0450-1
Examples
text <- c(
"Cats chase mice", "Dogs chase balls",
"Stocks and bonds trade", "Markets price shares"
)
embeddings <- rbind(c(1, 0), c(0.9, 0.1), c(0, 1), c(0.1, 0.9))
fitted <- topics(text, 2, embeddings = embeddings, keep_embeddings = TRUE)
topic_membership(fitted)
#> document_id topic probability rank
#> 1 1 1 1.000000e+00 1
#> 2 1 2 6.206785e-38 2
#> 3 2 1 1.000000e+00 1
#> 4 2 2 3.229897e-37 2
#> 5 3 2 1.000000e+00 1
#> 6 3 1 6.206785e-38 2
#> 7 4 2 1.000000e+00 1
#> 8 4 1 3.229897e-37 2