Найти наиболее часто встречающиеся слова в тексте в R

Question

Может кто-нибудь помочь мне с тем, как найти наиболее часто используемые два и три слова в тексте с помощью R?

Мой текст ...

text <- c("There is a difference between the common use of the term phrase and its technical use in linguistics. In common usage, a phrase is usually a group of words with some special idiomatic meaning or other significance, such as \"all rights reserved\", \"economical with the truth\", \"kick the bucket\", and the like. It may be a euphemism, a saying or proverb, a fixed expression, a figure of speech, etc. In grammatical analysis, particularly in theories of syntax, a phrase is any group of words, or sometimes a single word, which plays a particular role within the grammatical structure of a sentence. It does not have to have any special meaning or significance, or even exist anywhere outside of the sentence being analyzed, but it must function there as a complete grammatical unit. For example, in the sentence Yesterday I saw an orange bird with a white neck, the words an orange bird with a white neck form what is called a noun phrase, or a determiner phrase in some theories, which functions as the object of the sentence. Theorists of syntax differ in exactly what they regard as a phrase; however, it is usually required to be a constituent of a sentence, in that it must include all the dependents of the units that it contains. This means that some expressions that may be called phrases in everyday language are not phrases in the technical sense. For example, in the sentence I can't put up with Alex, the words put up with (meaning \'tolerate\') may be referred to in common language as a phrase (English expressions like this are frequently called phrasal verbs\ but technically they do not form a complete phrase, since they do not include Alex, which is the complement of the preposition with.") 

Answer 1

Вот простой базовый R подход к 5 самых частых слов:

head(sort(table(strsplit(gsub("[[:punct:]]", "", text), " ")), decreasing = TRUE), 5) 

#  a the  of  in phrase 
# 21  18  12  10  8 

Что она возвращает это целочисленный вектор с частотой использования и имена вектора соответствуют словам, которые были подсчитаны.

• gsub("[[:punct:]]", "", text) удалить знаки препинания, так как вы не хотите, чтобы подсчитать, что, я думаю
• strsplit(gsub("[[:punct:]]", "", text), " ") разбить строку на пространствах
• table() считать частоту уникальных элементов
• sort(..., decreasing = TRUE) сортировать их в по убыванию
• head(..., 5) для выбора только 5 самых популярных слов

Answer 2

Мы можем разделить слова и использовать таблицу, чтобы суммировать частоты:

words <- strsplit(text, "[ ,.\\(\\)\"]") 
sort(table(words, exclude = ""), decreasing = T) 

Answer 3

Пакет tidytext делает этот вид вещи довольно просто:

library(tidytext) 
library(dplyr) 

data_frame(text = text) %>% 
    unnest_tokens(word, text) %>% # split words 
    anti_join(stop_words) %>% # take out "a", "an", "the", etc. 
    count(word, sort = TRUE) %>% # count occurrences 

# Source: local data frame [73 x 2] 
# 
#   word  n 
#   (chr) (int) 
# 1  phrase  8 
# 2  sentence  6 
# 3  words  4 
# 4  called  3 
# 5  common  3 
# 6 grammatical  3 
# 7  meaning  3 
# 8   alex  2 
# 9   bird  2 
# 10 complete  2 
# ..   ... ... 

---

Если вопрос просит подсчетов биграмм и триграмм, tokenizers::tokenize_ngrams полезен:

library(tokenizers) 

tokenize_ngrams(text, n = 3L, n_min = 2L, simplify = TRUE) %>% # tokenize bigrams and trigrams 
    as_data_frame() %>% # structure 
    count(value, sort = TRUE) # count 

# Source: local data frame [531 x 2] 
# 
#   value  n 
#   (fctr) (int) 
# 1  of the  5 
# 2  a phrase  4 
# 3 the sentence  4 
# 4   as a  3 
# 5  in the  3 
# 6  may be  3 
# 7 a complete  2 
# 8 a phrase is  2 
# 9 a sentence  2 
# 10  a white  2 
# ..   ... ... 

Answer 4

Вашего текста:

text <- c("There is a difference between the common use of the term phrase and its technical use in linguistics. In common usage, a phrase is usually a group of words with some special idiomatic meaning or other significance, such as \"all rights reserved\", \"economical with the truth\", \"kick the bucket\", and the like. It may be a euphemism, a saying or proverb, a fixed expression, a figure of speech, etc. In grammatical analysis, particularly in theories of syntax, a phrase is any group of words, or sometimes a single word, which plays a particular role within the grammatical structure of a sentence. It does not have to have any special meaning or significance, or even exist anywhere outside of the sentence being analyzed, but it must function there as a complete grammatical unit. For example, in the sentence Yesterday I saw an orange bird with a white neck, the words an orange bird with a white neck form what is called a noun phrase, or a determiner phrase in some theories, which functions as the object of the sentence. Theorists of syntax differ in exactly what they regard as a phrase; however, it is usually required to be a constituent of a sentence, in that it must include all the dependents of the units that it contains. This means that some expressions that may be called phrases in everyday language are not phrases in the technical sense. For example, in the sentence I can't put up with Alex, the words put up with (meaning \'tolerate\') may be referred to in common language as a phrase (English expressions like this are frequently called phrasal verbs\ but technically they do not form a complete phrase, since they do not include Alex, which is the complement of the preposition with.") 

В Natural Language Processing, фраза 2-слов называются "би-грамм", и фраза 3-слов называются "три-грамм ", и так далее. Как правило, данная комбинация n-слов называется «n-gram».

Во-первых, мы устанавливаем пакет Ngram (доступный на CRAN)

# Install package "ngram" 
install.packages("ngram") 

Тогда мы найдем наиболее частые из двух слов и трех слов фразы

library(ngram) 

# To find all two-word phrases in the test "text": 
ng2 <- ngram(text, n = 2) 

# To find all three-word phrases in the test "text": 
ng3 <- ngram(text, n = 3) 

Наконец, мы будем печатать объекты (ngrams) с использованием различных способов, как показано ниже:

print(ng, output="truncated") 

print(ngram(x), output="full") 

get.phrasetable(ng) 

ngram::ngram_asweka(text, min=2, max=3) 

We C также использовать цепи Маркова лепетать новые последовательности:

# if we are using ng2 (bi-gram) 
lnth = 2 
babble(ng = ng2, genlen = lnth) 

# if we are using ng3 (tri-gram) 
lnth = 3 
babble(ng = ng3, genlen = lnth) 

Answer 5

Простейшим?

require(quanteda) 

# bi-grams 
topfeatures(dfm(text, ngrams = 2, verbose = FALSE)) 
##  of_the  a_phrase the_sentence  may_be   as_a  in_the in_common phrase_is 
##   5   4   4   3   3   3   2   2 
## is_usually  group_of 
##   2   2 

# for tri-grams 
topfeatures(dfm(text, ngrams = 3, verbose = FALSE)) 
##  a_phrase_is group_of_words of_a_sentence of_the_sentence for_example_in example_in_the 
##    2    2    2    2    2    2 
## in_the_sentence an_orange_bird orange_bird_with  bird_with_a 
#    2    2    2    2