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box::use( |
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quanteda[corpus, docnames, dfm, convert, dfm_tfidf, dfm_subset], |
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quanteda.textstats[textstat_simil], |
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utils[head], |
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spacyr[spacy_parse], |
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dplyr[mutate, filter, select], |
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) |
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|
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create_ref_corpus <- function(data_tab) { |
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corp <- corpus(data_tab, text_field = "description") |
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docnames(corp) <- data_tab$title |
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return(corp) |
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} |
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|
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spacy_pipeline <- function(corp) { |
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res <- corp |> spacy_parse() |
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res_tokens <- res |> |
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filter( |
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! pos %in% c("PUNCT", "PART", "NUM", "SYM") |
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) |> |
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mutate( |
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lemma = tolower(lemma) |
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) |> |
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as.tokens( |
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use_lemma = TRUE |
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) |> |
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tokens_remove(stopwords("en")) |
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|
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corp_dfm <- res_tokens |> dfm() |
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|
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saveRDS(corp_dfm, "./data/ref_corp_dfm.rds") |
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|
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corp_tfidf <- corp_dfm |> dfm_tfidf() |
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|
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saveRDS(corp_tfidf, "./data/ref_corp_tfidf.rds") |
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} |
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|
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get_recommendations <- function(corp_dfm, query_book_titles, simil_method = "ejaccard", how_many) { |
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query_dfm <- dfm_subset(corp_dfm, docname_ %in% query_book_titles) |
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|
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tstat <- textstat_simil( |
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query_dfm, corp_dfm, |
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margin = "documents", |
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method = simil_method |
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) |
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|
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stat_list <- as.list(tstat) |
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ordered <- sort(unlist(stat_list), decreasing = TRUE) |
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top_n <- head(ordered, n = how_many) |
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names(top_n) <- names(top_n) |> gsub(pattern = "\\..*$", replacement = "") |
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return(names(top_n)) |
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} |
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|
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parse_recommendations <- function(rec_book_names, data_tab) { |
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subset_books <- data_tab |> |
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filter( |
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title %in% rec_book_names |
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) |> |
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select( |
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title, average_rating, description, url, image_url, genres, author_name |
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) |
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return(subset_books) |
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} |
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