Как мне найти наиболее частые слова по каждому наблюдению в R?

Question

Я новичок в НЛП. Пожалуйста, не судите меня строго.

У меня очень большой набор данных по отзывам клиентов, моя цель - проанализировать отзывы. Я пометил слова в отзывах, удалил стоп-слова (SMART). Теперь мне нужно получить таблицу наиболее и менее часто используемых слов.

Код выглядит следующим образом:

library(tokenizers)
library(stopwords)
words_as_tokens <- 
     tokenize_words(dat$description, 
                    stopwords = stopwords(language = "en", source = "smart"))

Фрейм данных выглядит следующим образом: существует множество отзывов (переменных «описание») и клиентов, которым были предоставлены отзывы (каждый клиент не уникален, их можно повторять). Я хочу получить таблицу из 3 столбцов: а) имя клиента б) слово c) его частота. Этот «рейтинг» должен быть в порядке убывания.

Answer 1

Попробуйте это

library(tokenizers)
library(stopwords)
library(tidyverse)

# count freq of words
words_as_tokens <- setNames(lapply(sapply(dat$description, 
                                 tokenize_words, 
                                 stopwords = stopwords(language = "en", source = "smart")), 
                          function(x) as.data.frame(sort(table(x), TRUE), stringsAsFactors = F)), dat$name)

# tidyverse's job
df <- words_as_tokens %>%
  bind_rows(, .id = "name") %>%
  rename(word = x)

# output
df

#    name          word Freq
# 1  John    experience    2
# 2  John          word    2
# 3  John    absolutely    1
# 4  John        action    1
# 5  John        amazon    1
# 6  John     amazon.ae    1
# 7  John     answering    1
# ....
# 42 Alex         break    2
# 43 Alex          nice    2
# 44 Alex         times    2
# 45 Alex             8    1
# 46 Alex        accent    1
# 47 Alex        africa    1
# 48 Alex        agents    1
# ....

Данные

dat <- data.frame(name = c("John", "Alex"),
                  description = c("Unprecedented. The perfect word to describe Amazon. In every positive sense of that word! All because of one man - Jeff Bezos. What an entrepreneur! What a vision! This is from personal experience. Let me explain. I had given up all hope, after a horrible experience with Amazon.ae (formerly Souq.com) - due to a Herculean effort to get an order cancelled and the subsequent refund issued. I have never faced such a feedback-resistant team in my life! They were robotically answering my calls and sending me monotonous, unhelpful emails, followed by absolutely zero action!",
                                 "Not only does Amazon have great products but their Customer Service for the most part is wonderful. Although most times you are outsourced to a different country, I personally have found that when I call it's either South Africa or Philippines and they speak so well, understand me and my NY accent and are quite nice. Let’s face it. Most times you are calling CS with a problem or issue. These agents have to listen to 8 hours of complaints so they themselves need a break. No matter how annoyed I am I try to be on my best behavior and as nice as can be because they too need a break with how nasty we as a society can be."), stringsAsFactors = F)

Answer 2

Вы можете попробовать с quanteda , а также:

library(quanteda)
# define a corpus object to store your initial documents
mycorpus = corpus(dat$description)
# convert the corpus to a Document-Feature Matrix
mydfm = dfm( mycorpus, 
             tolower = TRUE, 
             remove = stopwords(),  # this removes English stopwords
             remove_punct = TRUE,   # this removes punctuation
             remove_numbers = TRUE, # this removes digits
             remove_symbol = TRUE,  # this removes symbols 
             remove_url = TRUE )     # this removes urls

# calculate word frequencies and return a data.frame
word_frequencies = textstat_frequency( mydfm )