Find semantically similar XKCD comics

Uses pre-computed latent semantic analysis (LSA) embeddings to find comics that are semantically similar to the given query. Unlike searchXKCD(), which matches keywords, this function captures meaning (e.g., "feeling lonely" can match comics about isolation even if they don't contain the word "lonely"). Run updateConfig() first to build the embeddings.

Usage

similarXKCD(query, n = 5L)

Arguments

query

A single string describing the topic or feeling to search for.

n

A single integer specifying the number of results to return. Defaults to 5.

Value

A data frame with columns num, date, title, alt, and similarity (cosine similarity score, at most 1), ordered by similarity. If no similar comics are found, an empty data frame is returned invisibly. The function will stop with an error if the embeddings have not been built.

Examples

if (FALSE) { # \dontrun{
updateConfig()
similarXKCD("space exploration")
} # }