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["index.html", "Natural Language Processing with R Chapter 1 Introduction", " Natural Language Processing with R Saif SHabou 2020-05-06 Chapter 1 Introduction This tutorial is based on the following books: https://www.tidytextmining.com/ https://github.com/jjallaire/deep-learning-with-r-notebooks The main goal is to introduce different techniques and methods for analyzing and mining textual data: Tokenization word embeddings text classification Sentiment analysis Topic modeling Term frequencies "],
["text-processing.html", "Chapter 2 Text processing 2.1 Text data 2.2 NLP applications 2.3 Tokenization 2.4 Stop words handeling 2.5 Words frequencies", " Chapter 2 Text processing 2.1 Text data Text data can be understood as sequences of characters or sequences of words 2.2 NLP applications Document classification Sentiment analysis Author identification Question answering Topic modeling 2.3 Tokenization It consists of defining the unit of analysis. This might include words, sequences of words, or entire sentences. We can tokenize text at verious units including: charcters, words, sentenses, lines, paragraphs, and n-grams. N-grams: An n-gram is a term in linguistics for a continious sequence of n items from a given sequence of text or speech. The item can be phonemes, syllabes, letters, or words depending on the application, but when most people talk about n-grames, they mean a group of n words. Examples: unigrams (“hello”, “day”, “work”), bigrams (“good day”, “hello world”), trigrams (“tou and me”, “day of work”). Bag of words: When we extract n-grams from a text documents, the collection of these n-grams are called bag of words, since the tokens have no specific order. # load text data library(janeaustenr) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(stringr) original_books <- austen_books() %>% group_by(book) %>% mutate(linenumber = row_number(), chapter = cumsum(str_detect(text, regex("^chapter [\\\\divxlc]", ignore_case = TRUE)))) %>% ungroup() original_books ## # A tibble: 73,422 x 4 ## text book linenumber chapter ## <chr> <fct> <int> <int> ## 1 "SENSE AND SENSIBILITY" Sense & Sensibility 1 0 ## 2 "" Sense & Sensibility 2 0 ## 3 "by Jane Austen" Sense & Sensibility 3 0 ## 4 "" Sense & Sensibility 4 0 ## 5 "(1811)" Sense & Sensibility 5 0 ## 6 "" Sense & Sensibility 6 0 ## 7 "" Sense & Sensibility 7 0 ## 8 "" Sense & Sensibility 8 0 ## 9 "" Sense & Sensibility 9 0 ## 10 "CHAPTER 1" Sense & Sensibility 10 1 ## # ... with 73,412 more rows # tokenization library(tidytext) tidy_books <- original_books %>% unnest_tokens(word, text) tidy_books ## # A tibble: 725,055 x 4 ## book linenumber chapter word ## <fct> <int> <int> <chr> ## 1 Sense & Sensibility 1 0 sense ## 2 Sense & Sensibility 1 0 and ## 3 Sense & Sensibility 1 0 sensibility ## 4 Sense & Sensibility 3 0 by ## 5 Sense & Sensibility 3 0 jane ## 6 Sense & Sensibility 3 0 austen ## 7 Sense & Sensibility 5 0 1811 ## 8 Sense & Sensibility 10 1 chapter ## 9 Sense & Sensibility 10 1 1 ## 10 Sense & Sensibility 13 1 the ## # ... with 725,045 more rows This function uses the tokenizer package to sperate each line of text into tokens. By default, it performs a word tokenization but we can select other options for chearcters, n-grams, sentences, lines, paragraphs… 2.4 Stop words handeling Often in text analysis, we will want to remove stop words; stop words are words that are not useful for an analysis, typically extremely common words such as “the”, “of”, “to”, and so forth in English. data(stop_words) tidy_books <- tidy_books %>% anti_join(stop_words) ## Joining, by = "word" 2.5 Words frequencies Find the most common words in all the books tidy_books %>% count(word, sort = TRUE) ## # A tibble: 13,914 x 2 ## word n ## <chr> <int> ## 1 miss 1855 ## 2 time 1337 ## 3 fanny 862 ## 4 dear 822 ## 5 lady 817 ## 6 sir 806 ## 7 day 797 ## 8 emma 787 ## 9 sister 727 ## 10 house 699 ## # ... with 13,904 more rows # plot the most common words library(ggplot2) tidy_books %>% count(word, sort = TRUE) %>% filter(n > 600) %>% mutate(word = reorder(word, n)) %>% ggplot(aes(word, n)) + geom_col() + xlab(NULL) + coord_flip() plotting a wordclouds library(wordcloud) ## Loading required package: RColorBrewer tidy_books %>% anti_join(stop_words) %>% count(word) %>% with(wordcloud(word, n, max.words = 100)) ## Joining, by = "word" "],
["Word-embeddings.html", "Chapter 3 Word embeddings 3.1 Vectorizing text 3.2 One-hot encoding 3.3 Word embeddings methods 3.4 Applications 3.5 references", " Chapter 3 Word embeddings This section is based on this book: https://github.com/jjallaire/deep-learning-with-r-notebooks 3.1 Vectorizing text It is the process of transforming text into numeric tensors. It consists of applying some tokenization scheme and then associating numeric vectors with the generated tokens. The generated vectos are packed into sequence tensors and fed into deep neural network. There are different ways to associate a vector within a token such as one-hot encoding and token embedding (typically used for words and called word embedding). 3.2 One-hot encoding It consists of one-hot encoding the words existing in a sentence based on the whole vocabulary.We create a vector with length equal to the vocabulary and we place a one in the index that corresponds to the word existing in the sentences. Then, we can concatenate the one-hot vectors for each word. This method is considered as inefficient since we obtain a sparse one-hot encoded vector (most indices are zero). One-hot encoding (source:https://www.tensorflow.org/tutorials/text/word_embeddings) library(keras) samples <- c("The cat sat on the mat.", "The dog ate my homework.") # Creates a tokenizer, configured to only take into account the 1,000 # most common words, then builds the word index. tokenizer <- text_tokenizer(num_words = 1000) %>% fit_text_tokenizer(samples) # Turns strings into lists of integer indices sequences <- texts_to_sequences(tokenizer, samples) # You could also directly get the one-hot binary representations. Vectorization # modes other than one-hot encoding are supported by this tokenizer. one_hot_results <- texts_to_matrix(tokenizer, samples, mode = "binary") # How you can recover the word index that was computed word_index <- tokenizer$word_index cat("Found", length(word_index), "unique tokens.\\n") ## Found 9 unique tokens. 3.3 Word embeddings methods The vectors obtained with one-hot encoding are binary, sparse and very high dimensional (same dimensionality of the number of words in the vocabulary). However, “word embeddings” are low-dimensional dense vectors (as oposite to sparse vectors). They are learned from data. They are commonly 256-dimensional, 512 dimensiona, or 1024-dimensional when dealing with large vocabularies. There are two methods for obtaining word embedings: Learn word embeddings jointly with a specified task (document classification, sentimenta alnaysis…). For this, we start with random word vectors and learn the word vectors in the same way that we learn the weights of a neural network. Use a “pre-trained” word embeddings and apply it to our specific task 3.3.1 Learn world embeddings Word embeddings aim t mapping human language into a geometric space in a way that geometric relationships between word vectors reflect the semantic relationships netween the words. For example, synonyms should be embedded into similar word vectors. We expect that geometric distance between any two word vectors represent semantic distance of the associated words. We can site among common meaningful geometric transformations in word embeddings the “gender vectors” and “plural vectors”. For example, by adding a “female vector” to the vector “king”, we obtain the vector “queen”. In the same way, by adding a “plural vector”, we obtain “kings”. It is hard to find the “ideal” word embedding space to perfectly map general human language. Word embedding performance depends on the task we are working on. A word embedding for Ensglish-language movie review sentiment analysis model may look very different from an English-language legal document classification model since the importance of some semantic relationships varies from task to task. Therefore, it is useful to learn a new embedding space with every new task. Keras offers the possibility of learning embeddings using layer_embedding(). # the embedding layer takes at least two arguments: # - the number of posssible tokens, here 1000 # - the dimensionality of the embeddings, here 64 embedding_layer = layer_embedding(input_dim = 1000, output_dim = 64) The embedding_layer is like a dictionary that maps integer indices to dense vectors. It takes as input a 2D tensor of integers, of shape (samples, sequence_length), where each entry is a sequence of integers. It generates a 3D floating-point tensor, of shape (samples, sequence_length, embedding_dimensionality. Let’s apply embedding_layer to the IMDB movie-review sentiment prediction task. We will consider only the top 10,000 most common words and cut off the review after only 20 words. The network will learn 8-dimensional embeddings for each of the 10,000 words, turn the input integer sequences (2D integer tensor) into embedded sequences (3D float tensor), flatten the tensor to 2D, and train a single dense layer on top for classification. library(keras) # Number of words to consider as features max_features = 10000 # cut texts after this number of words (among top max_features most common words) maxlen = 20 # load the data as lists of integers imdb = dataset_imdb(num_words = max_features) c(c(x_train, y_train), c(x_test, y_test)) %<-% imdb # This turns our lists of integers # into a 2D integer tensor of shape `(samples, maxlen)` x_train = pad_sequences(x_train, maxlen = maxlen) x_test = pad_sequences(x_test, maxlen = maxlen) library(keras) model = keras_model_sequential() %>% # we specify the maxmum input length to our embedding layer # so we can later flatten the embedded inputs layer_embedding(input_dim = 10000, output_dim = 8, input_length = maxlen) %>% # we flatten the 3D tensor of embeddings into a 2D tensor of shape (samples, maxlen * 8) layer_flatten() %>% # We add the classifier on top layer_dense(units = 1, activation = "sigmoid") model %>% compile( optimizer = "rmsprop", loss = "binary_crossentropy", metrics = c("acc") ) history = model %>% fit( x_train, y_train, epochs = 10, #10 batch_size = 32, validation_split = 0.2 ) plot(history) ## `geom_smooth()` using formula 'y ~ x' 3.3.2 Pre-trained word embeddings When we have little training data available to learn task-specific word embedding base on our vocabulary, it is preferable to use a pre-trained word embeddings. This technic is simular to transfer learning in image classification tasks, where we use a pretrained classifier. A pre-computed embedding is supposed to capture generic aspects of language structure. These word embeddings are trained based on co-occurence of words in sentences and documents within a large corpus of text. We can distinguish two main powerful word embeddings models: Word2Vec and GloVe. 3.3.2.1 Word2Vec 3.3.2.2 Glove 3.4 Applications 3.4.1 Using Skip-Gram We use the Amazon Fine Foods Reviews datset which consists of 500,000 reviews of Amazon fine food including product and user information, ratings, and narrative text. source: https://blogs.rstudio.com/tensorflow/posts/2017-12-22-word-embeddings-with-keras/ 3.4.1.1 Getting the data # we download the data download.file("https://snap.stanford.edu/data/finefoods.txt.gz", "finefoods.txt.gz") Now we load the plain text reviexs: library(readr) library(stringr) reviews <- read_lines("finefoods.txt.gz") reviews <- reviews[str_sub(reviews, 1, 12) == "review/text:"] reviews <- str_sub(reviews, start = 14) reviews <- iconv(reviews, to = "UTF-8") head(reviews, 2) ## [1] "I have bought several of the Vitality canned dog food products and have found them all to be of good quality. The product looks more like a stew than a processed meat and it smells better. My Labrador is finicky and she appreciates this product better than most." ## [2] "Product arrived labeled as Jumbo Salted Peanuts...the peanuts were actually small sized unsalted. Not sure if this was an error or if the vendor intended to represent the product as \\"Jumbo\\"." 3.4.1.2 Preprocessing We use text_tokenizer in order to transform each review into a sequence of integer tokens. By fixing num_words = 20000, we assign integer token to each of the 20,000 most common words (the other words will be assigned to token 0). library(keras) tokenizer = text_tokenizer(num_words = 20000) tokenizer %>% fit_text_tokenizer(reviews) #we can show the number of documents tokenizer$document_count ## [1] 568454 # we can show the word index list tokenizer$word_index %>% head() ## $the ## [1] 1 ## ## $i ## [1] 2 ## ## $and ## [1] 3 ## ## $a ## [1] 4 ## ## $to ## [1] 5 ## ## $it ## [1] 6 3.4.1.3 Skpi-Gram model In the skip-gram model, we use each word as input to a log-linear classifier, then predict words within a certain range before and after this word. It would be very compyationally expensive if we outpt a probability distribution over all the vocabulary for each target word we input in the model. Therefore, we will use negative sampling. It consists of sampling some words that don’t appear i the context and train a binary classifier to predict if the context word we passed is truly from the context or not. Let’s defin a generator function to yield batches for model training. This genratire function will receive a vector of texts, a tokenizer and the arguments for the skip-gram (the size of the window around each target word we exaine and how manu=y negative samples we ant to sample for each target word). library(reticulate) library(purrr) skipgrams_generator <- function(text, tokenizer, window_size, negative_samples) { gen <- texts_to_sequences_generator(tokenizer, sample(text)) function() { skip <- generator_next(gen) %>% skipgrams( vocabulary_size = tokenizer$num_words, window_size = window_size, negative_samples = 1 ) x <- transpose(skip$couples) %>% map(. %>% unlist %>% as.matrix(ncol = 1)) y <- skip$labels %>% as.matrix(ncol = 1) list(x, y) } } We define now the keras model using kers functional API. # Dimension of the embedding vector embedding_size = 128 # how many words to consider left and right skip_window = 5 # number of negative examples to sample for each word num_sampled = 1 We will write placeholders for the inputs using layer_input function input_target = layer_input(shape = 1) input_context = layer_input(shape = 1) Now let’s define the embedding matrix. The embedding is a matrix with dimensions (vocabulary, embedding_size) that acts as lookup table for the word vectors. embedding <- layer_embedding( input_dim = tokenizer$num_words + 1, output_dim = embedding_size, input_length = 1, name = "embedding" ) target_vector <- input_target %>% embedding() %>% layer_flatten() context_vector <- input_context %>% embedding() %>% layer_flatten() Now we define how the target_vector will be related to the context_vector in order to make the network output equal to 1 when the context word really appeared in the contexte and 0 otherwise. We want target_vector to be similar to the context_vector if they appeared in the same context. A typical measure of similarity is the cosine similarity. Give two vectors A and B the cosine similarity is defined by the Euclidean Dot product of A and B normalized by their magnitude. As we don’t need the similarity to be normalized inside the network, we will only calculate the dot product and then output a dense layer with sigmoid activation. dot_product <- layer_dot(list(target_vector, context_vector), axes = 1) output <- layer_dense(dot_product, units = 1, activation = "sigmoid") Let’s create and compile the model model <- keras_model(list(input_target, input_context), output) model %>% compile(loss = "binary_crossentropy", optimizer = "adam") summary(model) ## Model: "model" ## ________________________________________________________________________________ ## Layer (type) Output Shape Param # Connected to ## ================================================================================ ## input_1 (InputLayer) [(None, 1)] 0 ## ________________________________________________________________________________ ## input_2 (InputLayer) [(None, 1)] 0 ## ________________________________________________________________________________ ## embedding (Embedding) (None, 1, 128) 2560128 input_1[0][0] ## input_2[0][0] ## ________________________________________________________________________________ ## flatten_1 (Flatten) (None, 128) 0 embedding[0][0] ## ________________________________________________________________________________ ## flatten_2 (Flatten) (None, 128) 0 embedding[1][0] ## ________________________________________________________________________________ ## dot (Dot) (None, 1) 0 flatten_1[0][0] ## flatten_2[0][0] ## ________________________________________________________________________________ ## dense_1 (Dense) (None, 1) 2 dot[0][0] ## ================================================================================ ## Total params: 2,560,130 ## Trainable params: 2,560,130 ## Non-trainable params: 0 ## ________________________________________________________________________________ 3.4.1.4 Model training To fit the model we need to specify the number of training steps and the number of epochs. We will use only one epoch for time computation reasons. model %>% fit_generator( skipgrams_generator(reviews, tokenizer, skip_window, negative_samples), steps_per_epoch = 2000, epochs = 2 ) We can extract the embedding matrix from the model using the get_weights() function. library(dplyr) embedding_matrix <- get_weights(model)[[1]] words <- data_frame( word = names(tokenizer$word_index), id = as.integer(unlist(tokenizer$word_index)) ) ## Warning: `data_frame()` is deprecated as of tibble 1.1.0. ## Please use `tibble()` instead. ## This warning is displayed once every 8 hours. ## Call `lifecycle::last_warnings()` to see where this warning was generated. words <- words %>% filter(id <= tokenizer$num_words) %>% arrange(id) row.names(embedding_matrix) <- c("UNK", words$word) dim(embedding_matrix) ## [1] 20001 128 head(embedding_matrix) ## [,1] [,2] [,3] [,4] [,5] [,6] ## UNK 0.0170815 -0.001318716 0.04716846 0.0001694448 -0.01495628 0.01284777 ## the 0.1174329 -0.383204609 -0.14486948 0.2978044450 0.17878745 -0.28364837 ## i 0.1669428 -0.247277036 -0.21312974 0.3762883544 0.28889671 -0.30660424 ## and 0.1605144 -0.230475992 -0.17357136 0.3322810829 0.20980199 -0.25617325 ## a 0.1201052 -0.146714032 -0.15440495 0.3936571181 0.22841439 -0.16955599 ## to 0.1918591 -0.273127079 -0.17826691 0.3529382646 0.28203753 -0.24766697 ## [,7] [,8] [,9] [,10] [,11] ## UNK -0.007465005 -0.01517171 0.03539601 0.04036165 -0.0001689792 ## the 0.299164832 -0.30537584 -0.36950541 -0.28876448 -0.2681583762 ## i 0.362938732 -0.26686862 -0.34998602 -0.23723570 -0.2789547443 ## and 0.310600638 -0.21819803 -0.34236768 -0.32119495 -0.1779417843 ## a 0.278418452 -0.25963432 -0.35211003 -0.31251714 -0.2506726086 ## to 0.347074360 -0.16523284 -0.32140639 -0.28436336 -0.2654981911 ## [,12] [,13] [,14] [,15] [,16] [,17] ## UNK -0.0004024878 -0.01031651 -0.0329108 -0.03570246 -0.01811307 -0.02383759 ## the 0.2796629667 0.31438842 -0.2472963 0.20960861 -0.28063810 0.17194803 ## i 0.3474592566 0.33163065 -0.3115005 0.29770941 -0.32637006 0.09885775 ## and 0.3099879324 0.28850329 -0.2122750 0.25450185 -0.35340777 0.15842982 ## a 0.3022259772 0.33895627 -0.2288224 0.27653986 -0.23382780 0.15667632 ## to 0.3335136473 0.27410549 -0.3449964 0.31313083 -0.33614126 0.02599920 ## [,18] [,19] [,20] [,21] [,22] [,23] ## UNK 0.001930606 -0.04853332 0.03184437 -0.03507299 0.03099043 -0.01559343 ## the 0.249449074 0.38885596 -0.10571286 0.03303542 -0.01977662 0.12713103 ## i 0.266584009 0.32550138 -0.14738728 0.10654508 0.04477475 0.28375289 ## and 0.281591505 0.33543691 -0.12878042 0.19387311 0.08222436 0.23661953 ## a 0.213139340 0.28658631 -0.13082723 0.11910986 0.03216605 0.19973753 ## to 0.144440114 0.35665330 -0.12372198 0.12668315 0.19030270 0.19488603 ## [,24] [,25] [,26] [,27] [,28] [,29] ## UNK -0.02061616 0.01281368 -0.03231498 0.007334851 0.03719245 0.03511449 ## the -0.17688274 -0.32929045 -0.29310527 0.289794803 0.13527869 -0.27275386 ## i -0.23470533 -0.32637039 -0.26748180 0.318160236 0.03208359 -0.21226105 ## and -0.25599492 -0.34276465 -0.33586788 0.217786700 0.02351790 -0.27456364 ## a -0.17987984 -0.37211826 -0.33599785 0.294163078 -0.06172014 -0.27303630 ## to -0.25984651 -0.32187936 -0.29270798 0.297974795 -0.07057865 -0.25982314 ## [,30] [,31] [,32] [,33] [,34] [,35] ## UNK -0.01691567 0.0008091219 -0.03200240 -0.006604362 -0.03469641 -0.03299238 ## the 0.25344208 0.1719853282 0.07652652 -0.321915329 0.23948634 0.01470774 ## i 0.22992177 0.0279782731 0.08346167 -0.304561466 0.12635729 0.02191944 ## and 0.18178248 0.1102448180 0.08257330 -0.269136578 0.20793088 -0.09406497 ## a 0.18358734 0.2180162519 0.05090325 -0.267941982 0.16697714 0.03600145 ## to 0.25895324 -0.0176757276 0.13935305 -0.208568946 0.19998248 -0.05754858 ## [,36] [,37] [,38] [,39] [,40] [,41] ## UNK 0.03315267 0.04222684 -0.02326957 -0.03361871 -0.0365397 -0.043440260 ## the -0.28853074 0.37395093 0.20946701 0.15802002 0.2907013 -0.010865721 ## i -0.36051789 0.36837849 0.31315809 0.19938451 0.2936096 -0.131256387 ## and -0.27480209 0.37666577 0.28615251 0.18097939 0.2794661 -0.006140751 ## a -0.27293894 0.33773720 0.32223794 0.22417280 0.2404891 -0.039677981 ## to -0.30174917 0.30440938 0.29927579 0.14390787 0.2480671 -0.093178861 ## [,42] [,43] [,44] [,45] [,46] [,47] ## UNK 0.01521539 0.00477301 0.0318060 -0.02853984 0.0437434 -0.01864365 ## the 0.30115995 0.05320331 0.3138930 0.10096217 -0.2055138 -0.20989250 ## i 0.35655829 -0.11047912 0.3219035 0.07911555 -0.2606398 -0.12337824 ## and 0.33989990 0.04763594 0.2755820 0.04128102 -0.1459931 -0.06884515 ## a 0.35712439 0.04051579 0.2893077 0.08757305 -0.1562458 -0.03052964 ## to 0.36761856 -0.03275176 0.3164682 0.02745412 -0.2089933 -0.10701507 ## [,48] [,49] [,50] [,51] [,52] [,53] ## UNK 0.04101357 0.03473446 0.04457737 -0.00114752 -0.04412064 -0.03156789 ## the 0.02520799 -0.25871646 -0.37619755 0.38879156 -0.25250259 -0.39248210 ## i 0.01821698 -0.22392237 -0.32261470 0.35049382 -0.27527589 -0.38099816 ## and -0.05184063 -0.16932927 -0.33808553 0.37406263 -0.32858366 -0.34304696 ## a -0.09030625 -0.29941657 -0.30359963 0.29772824 -0.26200148 -0.27937773 ## to -0.01727733 -0.24934988 -0.25524116 0.35741013 -0.29380852 -0.32908934 ## [,54] [,55] [,56] [,57] [,58] [,59] ## UNK 0.006125987 -0.02187279 0.03910586 -0.01629753 0.04972812 0.03518805 ## the -0.310638994 0.36762497 -0.10685650 0.34639886 0.35899246 -0.36781129 ## i -0.349121183 0.37237552 -0.21593590 0.31275219 0.42300200 -0.30578846 ## and -0.344224006 0.30466965 -0.15587379 0.32809687 0.32287005 -0.33381802 ## a -0.350583643 0.32805666 -0.11700507 0.27578056 0.28753132 -0.29372326 ## to -0.369859546 0.37795553 -0.16025186 0.28375304 0.33897606 -0.31854972 ## [,60] [,61] [,62] [,63] [,64] [,65] ## UNK 0.04667592 -0.01325144 0.04597353 0.007199753 0.04204318 0.004700471 ## the 0.27253532 0.14934577 0.30485842 -0.316160858 -0.27030918 -0.083663106 ## i 0.23735967 0.11750556 0.31638011 -0.297600389 -0.21566036 -0.096772537 ## and 0.18881306 0.06630570 0.31316441 -0.284757823 -0.26432148 -0.110183306 ## a 0.23069674 0.08311401 0.23403980 -0.271946669 -0.19995712 -0.172960505 ## to 0.29635924 0.04342796 0.25315505 -0.227531537 -0.28717574 -0.092312992 ## [,66] [,67] [,68] [,69] [,70] [,71] ## UNK -0.02999171 -0.01793531 0.04247624 0.002061225 -0.02451816 0.02576012 ## the -0.11517787 0.12338851 -0.04987039 -0.034545448 -0.06101116 0.05578730 ## i 0.06015281 0.01339131 0.00329218 -0.055252384 -0.08577745 0.12629287 ## and -0.07259820 -0.03888071 0.01285036 -0.006432456 -0.02521065 0.09755906 ## a 0.01760340 -0.11909988 0.08444444 -0.040843900 0.01344274 0.11524677 ## to 0.04376663 -0.09150210 0.03593209 -0.064415947 -0.08948220 0.10198851 ## [,72] [,73] [,74] [,75] [,76] [,77] ## UNK 0.008057524 -0.01873593 0.004101884 -0.02451124 -0.01045469 -0.007409252 ## the -0.210249856 0.24684344 -0.178273425 0.12195436 -0.14832009 -0.229609445 ## i -0.339593619 0.25152388 -0.016085856 0.11553814 -0.24507025 -0.292536318 ## and -0.210022196 0.24458480 -0.108235233 0.12692633 -0.15382548 -0.244550496 ## a -0.300737977 0.19017297 -0.177162036 0.09492953 -0.15454216 -0.303135395 ## to -0.386955142 0.19876392 -0.071368732 0.08350065 -0.25868317 -0.269913286 ## [,78] [,79] [,80] [,81] [,82] [,83] ## UNK 0.02775276 0.02282996 0.00179093 0.001864087 -0.02228262 0.02817461 ## the -0.35284138 0.27658230 -0.26196435 0.198721379 -0.04214166 -0.37608743 ## i -0.37237868 0.28755802 -0.29970258 0.303372979 -0.02192708 -0.34754741 ## and -0.32777765 0.19116035 -0.25336358 0.194186181 0.01598708 -0.33635759 ## a -0.30274969 0.23791181 -0.28293476 0.306074739 -0.07584713 -0.36234859 ## to -0.32587340 0.23608799 -0.21567842 0.323878646 -0.03771929 -0.36036131 ## [,84] [,85] [,86] [,87] [,88] [,89] ## UNK -0.003465034 0.01472980 0.04781802 -0.007102478 0.04773111 -0.01337481 ## the 0.288525522 -0.08883095 -0.22900291 -0.169002935 0.16737778 -0.19447237 ## i 0.313215643 -0.19014385 -0.28267121 -0.305975825 0.15154187 -0.13293040 ## and 0.332025588 -0.14811261 -0.27446076 -0.231908619 0.10666943 -0.24451743 ## a 0.283321589 -0.14821632 -0.25289920 -0.340203524 0.18615662 -0.28705639 ## to 0.297496915 -0.17047712 -0.22169787 -0.278337449 0.18933631 -0.13048588 ## [,90] [,91] [,92] [,93] [,94] [,95] ## UNK -0.01147924 -0.00587051 0.04609055 -0.0001483187 0.04964909 -0.01832782 ## the -0.32560977 -0.17560473 -0.25437045 0.1731803566 0.28145239 0.37646624 ## i -0.37563258 -0.22397560 -0.19170810 0.2082554251 0.29436311 0.39287877 ## and -0.30389935 -0.06919212 -0.20327331 0.2024096847 0.29195097 0.38377520 ## a -0.28205562 -0.17666516 -0.23301259 0.2374358177 0.33052328 0.34986836 ## to -0.30297431 -0.15620883 -0.13064004 0.2584783733 0.28516826 0.32652554 ## [,96] [,97] [,98] [,99] [,100] [,101] ## UNK 0.007632814 0.005672503 -0.03266094 -0.03422055 0.01324456 0.02385963 ## the 0.302464426 0.110885777 -0.25989842 0.38501096 -0.21835038 -0.20524764 ## i 0.296282232 0.233367577 -0.20001604 0.38733375 -0.24814697 -0.15178022 ## and 0.310684830 0.118035696 -0.17034222 0.33256108 -0.19430402 -0.18163270 ## a 0.337718070 0.206213146 -0.24668492 0.32690060 -0.22618128 -0.18086091 ## to 0.365883678 0.275636226 -0.25219843 0.31734639 -0.24367192 -0.09583790 ## [,102] [,103] [,104] [,105] [,106] [,107] ## UNK -0.04407374 -0.03826056 0.001508869 -0.01589622 -0.02959334 0.02968781 ## the 0.19846255 0.21909449 -0.077848770 0.24505678 0.34444517 0.16305423 ## i 0.28018263 0.23005924 -0.110724866 0.19277629 0.37343127 0.09825584 ## and 0.24288949 0.20457232 -0.084622771 0.14417087 0.26135206 0.06721097 ## a 0.29050061 0.30875629 -0.190523997 0.18856290 0.30580518 0.14732127 ## to 0.26581255 0.28777468 -0.149579942 0.14878766 0.26804748 0.02433010 ## [,108] [,109] [,110] [,111] [,112] [,113] ## UNK 0.032761965 -0.03484957 0.04724887 -0.01673875 0.04846228 0.01651904 ## the 0.072625257 -0.22000135 0.29008779 0.21206540 -0.08905456 0.30720186 ## i 0.137971386 -0.30346900 0.32460919 0.24432959 -0.04133808 0.37891588 ## and 0.067033872 -0.28801164 0.25215000 0.20263389 -0.07493820 0.23705998 ## a 0.008180861 -0.27213508 0.28106183 0.26201847 -0.13696785 0.24244435 ## to 0.133614138 -0.27308589 0.25494623 0.20437345 0.09366596 0.26203859 ## [,114] [,115] [,116] [,117] [,118] [,119] ## UNK 0.03307271 0.006484438 0.04113635 -0.04053799 0.03232178 -0.01916174 ## the 0.37055835 0.000467650 -0.33548412 -0.06496470 -0.36024690 -0.24840827 ## i 0.32405031 0.068578012 -0.42496726 -0.02733710 -0.32280901 -0.25782296 ## and 0.37622270 0.064659454 -0.35911053 0.03896505 -0.33915231 -0.27443057 ## a 0.37359667 0.117730454 -0.29758999 0.10123279 -0.32194731 -0.21069331 ## to 0.32364631 0.143883005 -0.31454709 0.14705095 -0.27599317 -0.22744414 ## [,120] [,121] [,122] [,123] [,124] [,125] ## UNK -0.00625832 -0.04583708 0.01545075 0.03551065 -0.02845714 -0.024710560 ## the 0.23234977 0.37063292 -0.06700579 0.18953991 -0.30410570 -0.069787078 ## i 0.36668271 0.31222266 -0.09937420 0.27160498 -0.34567764 -0.001867783 ## and 0.33828005 0.33334142 -0.06587321 0.30039573 -0.28826779 0.067308143 ## a 0.30670270 0.39588228 -0.05656852 0.15388277 -0.30768496 0.107387684 ## to 0.30960637 0.33873239 -0.03573503 0.23836206 -0.29423913 0.053528536 ## [,126] [,127] [,128] ## UNK 0.03197979 0.02286074 -0.04385829 ## the 0.36302400 0.25149447 0.23504810 ## i 0.39596969 0.26540828 0.26525143 ## and 0.33469549 0.24421459 0.21839896 ## a 0.31807998 0.26717538 0.26289040 ## to 0.36336777 0.25848782 0.25542757 3.4.1.5 Understanding the embeddings We can now find words that are close to each other in the embedding. We will use the cosine similarity, since this is what we trained the model to minimize. library(text2vec) ## ## Attaching package: 'text2vec' ## The following objects are masked from 'package:keras': ## ## fit, normalize find_similar_words <- function(word, embedding_matrix, n = 5) { similarities <- embedding_matrix[word, , drop = FALSE] %>% sim2(embedding_matrix, y = ., method = "cosine") similarities[,1] %>% sort(decreasing = TRUE) %>% head(n) } find_similar_words("delicious", embedding_matrix) ## delicious bought green texture price ## 1.0000000 0.9809152 0.9789813 0.9783692 0.9781281 find_similar_words("cats", embedding_matrix) ## cats chocolate best too bag ## 1.0000000 0.9785330 0.9782802 0.9773057 0.9770379 The t-SNE algorithm can be used to visualize the embeddings. Because of time constraints we will only use it with the first 500 words. o understand more about the t-SNE method see the article: https://distill.pub/2016/misread-tsne/ library(Rtsne) library(ggplot2) library(plotly) ## ## Attaching package: 'plotly' ## The following object is masked from 'package:ggplot2': ## ## last_plot ## The following object is masked from 'package:stats': ## ## filter ## The following object is masked from 'package:graphics': ## ## layout tsne <- Rtsne(embedding_matrix[2:500,], perplexity = 50, pca = FALSE) tsne_plot <- tsne$Y %>% as.data.frame() %>% mutate(word = row.names(embedding_matrix)[2:500]) %>% ggplot(aes(x = V1, y = V2, label = word)) + geom_text(size = 3) tsne_plot 3.4.2 Using GloVe source: http://text2vec.org/glove.html In this example, we will use GloVe to test how much it captures linguistic regularities. By takig the word vectors corresponding to the words: “Paris”, “france”, and “gremany”, we are supposed to obtain “berlin” as closest resulting vector. \\(vector("paris") - vector("france) + vector("germany")\\) we will use the wikpiedeia data which is used as a demo by wor2vec. # download data # download.file("http://mattmahoney.net/dc/text8.zip", "D:/NLP/NLP-book/data/text8.zip") # unzip("D:/NLP/NLP-book/data/text8.zip", files = "text8", exdir = "D:/NLP/NLP-book/data/text8") # load data wiki = readLines("D:/NLP/NLP-book/data/text8/text8", n = 1, warn = FALSE) Now, we create a vocabulary constituted of set of words for wich we want to learn word vectors. # Create iterator over tokens tokens <- space_tokenizer(wiki) # Create vocabulary. Terms will be unigrams (simple words). it = itoken(tokens, progressbar = FALSE) vocab <- create_vocabulary(it) str(vocab) ## Classes 'text2vec_vocabulary' and 'data.frame': 253854 obs. of 3 variables: ## $ term : chr "aaaaaacceglllnorst" "aaaaaaccegllnorrst" "aaaaaah" "aaaaaalmrsstt" ... ## $ term_count: int 1 1 1 1 1 1 1 1 1 1 ... ## $ doc_count : int 1 1 1 1 1 1 1 1 1 1 ... ## - attr(*, "ngram")= Named int 1 1 ## ..- attr(*, "names")= chr "ngram_min" "ngram_max" ## - attr(*, "document_count")= int 1 ## - attr(*, "stopwords")= chr ## - attr(*, "sep_ngram")= chr "_" We should remove unbommon words since it is not meaningful to keep word vector for word that we saw only once in the entire corpus. In this example we will keep only ords which apear at least five times. vocab <- prune_vocabulary(vocab, term_count_min = 5L) min(vocab$term_count) ## [1] 5 Now we have 71,290 terms in the vocabulary and are ready to construct term-co-occurence matrix (TCM). # Use our filtered vocabulary vectorizer <- vocab_vectorizer(vocab) # use window of 5 for context words tcm <- create_tcm(it, vectorizer, skip_grams_window = 5L) tcm[1:10, 1:10] ## 10 x 10 sparse Matrix of class "dgTMatrix" ## [[ suppressing 10 column names 'aapke', 'ababda', 'abakumov' ... ]] ## ## aapke . . . . . . . . . . ## ababda . . . . . . . . . . ## abakumov . . . . . . . . . . ## abalones . . . . . . . . . . ## abano . . . . . . . . . . ## abati . . . . . . . . . . ## abbates . . . . . . 1.25 . . . ## abbesses . . . . . . . . . . ## abderus . . . . . . . . 1 . ## abdications . . . . . . . . . . Now we have a TCM matrix and can factorize it via the GloVe algorithm. glove = GlobalVectors$new(rank = 50, x_max = 10) wv_main = glove$fit_transform(tcm, n_iter = 10, convergence_tol = 0.01) ## INFO [23:50:33.466] epoch 1, loss 0.1745 ## INFO [23:50:47.080] epoch 2, loss 0.1224 ## INFO [23:51:00.388] epoch 3, loss 0.1083 ## INFO [23:51:13.728] epoch 4, loss 0.1004 ## INFO [23:51:27.567] epoch 5, loss 0.0953 ## INFO [23:51:40.924] epoch 6, loss 0.0917 ## INFO [23:51:54.514] epoch 7, loss 0.0889 ## INFO [23:52:08.667] epoch 8, loss 0.0868 ## INFO [23:52:22.352] epoch 9, loss 0.0850 ## INFO [23:52:35.699] epoch 10, loss 0.0836 dim(wv_main) ## [1] 71290 50 Note that model learns two sets of word vectors - main and context. Essentially they are the same since model is symmetric. From our experience learning two sets of word vectors leads to higher quality embeddings. wv_context = glove$components dim(wv_context) ## [1] 50 71290 While both of word-vectors matrices can be used as result it usually better (idea from GloVe paper) to average or take a sum of main and context vector: word_vectors = wv_main + t(wv_context) We can find the closest word vectors for our paris - france + germany example: berlin = word_vectors["paris", , drop = FALSE] - word_vectors["france", , drop = FALSE] + word_vectors["germany", , drop = FALSE] cos_sim = sim2(x = word_vectors, y = berlin, method = "cosine", norm = "l2") head(sort(cos_sim[,1], decreasing = TRUE), 5) ## paris berlin bonn london leipzig ## 0.7771973 0.7295444 0.6742783 0.6663386 0.6612857 3.5 references http://pablobarbera.com/ECPR-SC105/code/16-word-embeddings.html https://code.google.com/archive/p/word2vec/ https://m-clark.github.io/text-analysis-with-R/word-embeddings.html#wikipedia https://juliasilge.com/blog/gender-pronouns/ https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/ https://machinelearningmastery.com/what-are-word-embeddings/ https://rpubs.com/JanpuHou/396443 https://mran.microsoft.com/snapshot/2016-03-05/web/packages/text2vec/vignettes/text-vectorization.html https://cbail.github.io/textasdata/word2vec/rmarkdown/word2vec.html https://www.jla-data.net/eng/vocabulary-based-text-classification/ http://text2vec.org/glove.html http://text2vec.org/similarity.html https://www.r-craft.org/r-news/get-busy-with-word-embeddings-an-introduction/ "],
["text-classification.html", "Chapter 4 Text classification 4.1 Load the data 4.2 Prepare the data for neural network 4.3 Building the model 4.4 Testing the model 4.5 Reference", " Chapter 4 Text classification This tutorial classifies movie reviews as positive or negative using the text of the review. We’ll use the IMDB dataset that contains the text of 50,000 movie reviews from the Internet Movie Database. These are split into 25,000 reviews for training and 25,000 reviews for testing. The training and testing sets are balanced, meaning they contain an equal number of positive and negative reviews. library(keras) library(tensorflow) 4.1 Load the data We will keep only the op 10,000 most frequently occuring words in the training data. imdb <- dataset_imdb(num_words = 10000) train_data <- imdb$train$x train_labels <- imdb$train$y test_data <- imdb$test$x test_labels <- imdb$test$y The obtained train_data and test_data are lists of review. Each review is a list of word indices. str(train_data[[1]]) ## int [1:218] 1 14 22 16 43 530 973 1622 1385 65 ... The obtained train_labels and test_labels are lists of 0 (negatove revieuw) and 1 (positive review). str(train_labels[[1]]) ## int 1 We can decode the words index back to text words in this way: # Named list mapping words to an integer index. word_index <- dataset_imdb_word_index() reverse_word_index <- names(word_index) names(reverse_word_index) <- word_index # Decodes the review. Note that the indices are offset by 3 because 0, 1, and # 2 are reserved indices for "padding," "start of sequence," and "unknown." decoded_review <- sapply(train_data[[1]], function(index) { word <- if (index >= 3) reverse_word_index[[as.character(index - 3)]] if (!is.null(word)) word else "?" }) cat(decoded_review) ## ? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all 4.2 Prepare the data for neural network Since we can’t feed a list of integers into a neural network, we need to transfomr our ists into tensors. There are two options to turn lists of integers into tensors: Pad our lists in way that they have the same length. We pad them into an integer tensor of shape samples, word_indices. Then, we use a first layer in our network a layer that can handle such integer tensors, like the embedding_layer. One-hot encoding our lists by transforming them into vectors of 0s and 1s. Then, we could use the obtained sparce vectors as the first layer. We will test this solution in the tutorial in order to learn how to vectorize manually the data. vectorize_sequences <- function(sequences, dimension = 10000) { # Creates an all-zero matrix of shape (length(sequences), dimension) results <- matrix(0, nrow = length(sequences), ncol = dimension) for (i in 1:length(sequences)) # Sets specific indices of results[i] to 1s results[i, sequences[[i]]] <- 1 results } x_train <- vectorize_sequences(train_data) x_test <- vectorize_sequences(test_data) Here’s what the samples look like now: str(x_train[1,]) ## num [1:10000] 1 1 0 1 1 1 1 1 1 0 ... We should also convert your labels from integer to numeric y_train <- as.numeric(train_labels) y_test <- as.numeric(test_labels) 4.3 Building the model We will use a simple stack of fully connected dense layers with relu activation. library(keras) model <- keras_model_sequential() %>% layer_dense(units = 16, activation = "relu", input_shape = c(10000)) %>% layer_dense(units = 16, activation = "relu") %>% layer_dense(units = 1, activation = "sigmoid") We compile the model model %>% compile( optimizer = optimizer_rmsprop(lr=0.001), loss = "binary_crossentropy", metrics = c("accuracy") ) In order to monitor during training the accuracy of the model on data it has never seen before, you’ll create a validation set by setting apart 10,000 samples from the original training data. val_indices <- 1:10000 x_val <- x_train[val_indices,] partial_x_train <- x_train[-val_indices,] y_val <- y_train[val_indices] partial_y_train <- y_train[-val_indices] Now we train the odel over 10 epochs, in mini-batches of 512 samples. In order to monitor loss ad accuracy on the validation set, we pass the validation data as validation_data argument. history <- model %>% keras::fit( partial_x_train, partial_y_train, epochs = 10, batch_size = 512, validation_data = list(x_val, y_val) ) plot(history) 4.4 Testing the model We saw in the last section that the model performance decrease after 4 epochs and starts to overfitting. So, we can decide to stop training after 4 epochs to avoid overfitting. We will train the model from scratch with 4 epochs and evaluate it with the test data. model <- keras_model_sequential() %>% layer_dense(units = 16, activation = "relu", input_shape = c(10000)) %>% layer_dense(units = 16, activation = "relu") %>% layer_dense(units = 1, activation = "sigmoid") model %>% compile( optimizer = "rmsprop", loss = "binary_crossentropy", metrics = c("accuracy") ) model %>% keras::fit(x_train, y_train, epochs = 4, batch_size = 512) results <- model %>% evaluate(x_test, y_test) results 4.5 Reference Chollet & Allaire (2017, Dec. 7). RStudio AI Blog: Deep Learning for Text Classification with Keras. Retrieved from https://blogs.rstudio.com/tensorflow/posts/2017-12-07-text-classification-with-keras/ "],
["RNN.html", "Chapter 5 Reccurent Neural Networks (RNN) 5.1 Understanding Recurrent Neural Network 5.2 RNN with Keras 5.3 LSTM with Keras", " Chapter 5 Reccurent Neural Networks (RNN) 5.1 Understanding Recurrent Neural Network 5.2 RNN with Keras library(keras) max_features <- 10000 # Number of words to consider as features maxlen <- 500 # Cuts off texts after this many words (among the max_features most common words) batch_size <- 32 cat("Loading data...\\n") ## Loading data... # load data imdb <- dataset_imdb(num_words = max_features) c(c(input_train, y_train), c(input_test, y_test)) %<-% imdb cat(length(input_train), "train sequences\\n") ## 25000 train sequences cat(length(input_test), "test sequences") ## 25000 test sequences # pad sequences input_train <- pad_sequences(input_train, maxlen = maxlen) input_test <- pad_sequences(input_test, maxlen = maxlen) cat("input_train shape:", dim(input_train), "\\n") ## input_train shape: 25000 500 let’s train the model model <- keras_model_sequential() %>% layer_embedding(input_dim = max_features, output_dim = 32) %>% layer_simple_rnn(units = 32) %>% layer_dense(units = 1, activation = "sigmoid") model %>% compile( optimizer = "rmsprop", loss = "binary_crossentropy", metrics = c("acc") ) history <- model %>% keras::fit( input_train, y_train, epochs = 10, batch_size = 128, validation_split = 0.2 ) plot(history) ## `geom_smooth()` using formula 'y ~ x' 5.3 LSTM with Keras model <- keras_model_sequential() %>% layer_embedding(input_dim = max_features, output_dim = 32) %>% layer_lstm(units = 32) %>% layer_dense(units = 1, activation = "sigmoid") model %>% compile( optimizer = "rmsprop", loss = "binary_crossentropy", metrics = c("acc") ) history <- model %>% keras::fit( input_train, y_train, epochs = 5, batch_size = 128, validation_split = 0.2 ) plot(history) https://jjallaire.github.io/deep-learning-with-r-notebooks/notebooks/6.3-advanced-usage-of-recurrent-neural-networks.nb.html "],
["sentiment-analysis.html", "Chapter 6 Sentiment Analysis 6.1 The “Sentiments” dataset 6.2 Application 6.3 References:", " Chapter 6 Sentiment Analysis 6.1 The “Sentiments” dataset There are several ethods and dictionaries that we can use for evaluating the opinion or emotion in text. We can site: AFINN: http://www2.imm.dtu.dk/pubdb/views/publication_details.php?id=6010 bing: https://www.cs.uic.edu/~liub/FBS/sentiment-analysis.html nrc: http://saifmohammad.com/WebPages/NRC-Emotion-Lexicon.htm These datsets contain many English words with assigned scores for positive/negative sentiment and emotions (joy, anger, sadness…). These data are mainly constructed via crowdsourcing tools (for example: Amazon Mechanical Turk) and valiated based by using available dataset such as movie review, Twitter… They are only based on unigrams so they don’t take inot account negation (“no good”, “not sad”…). library(tidytext) library(textdata) ## ## Attaching package: 'textdata' ## The following object is masked from 'package:keras': ## ## dataset_imdb get_sentiments("bing") ## # A tibble: 6,786 x 2 ## word sentiment ## <chr> <chr> ## 1 2-faces negative ## 2 abnormal negative ## 3 abolish negative ## 4 abominable negative ## 5 abominably negative ## 6 abominate negative ## 7 abomination negative ## 8 abort negative ## 9 aborted negative ## 10 aborts negative ## # ... with 6,776 more rows get_sentiments("nrc") ## # A tibble: 13,901 x 2 ## word sentiment ## <chr> <chr> ## 1 abacus trust ## 2 abandon fear ## 3 abandon negative ## 4 abandon sadness ## 5 abandoned anger ## 6 abandoned fear ## 7 abandoned negative ## 8 abandoned sadness ## 9 abandonment anger ## 10 abandonment fear ## # ... with 13,891 more rows 6.2 Application We can implement sentiment analysis by joinin text data with setiment dataset. Here is an example for finding the most common “joy” words in “Emma” book from “jane austen” autor ny using the “nrc” lexicon. We prepare the text data by getting words tokens tidy_books <- austen_books() %>% group_by(book) %>% mutate(linenumber = row_number(), chapter = cumsum(str_detect(text, regex("^chapter [\\\\divxlc]", ignore_case = TRUE)))) %>% ungroup() %>% unnest_tokens(word, text) tidy_books ## # A tibble: 725,055 x 4 ## book linenumber chapter word ## <fct> <int> <int> <chr> ## 1 Sense & Sensibility 1 0 sense ## 2 Sense & Sensibility 1 0 and ## 3 Sense & Sensibility 1 0 sensibility ## 4 Sense & Sensibility 3 0 by ## 5 Sense & Sensibility 3 0 jane ## 6 Sense & Sensibility 3 0 austen ## 7 Sense & Sensibility 5 0 1811 ## 8 Sense & Sensibility 10 1 chapter ## 9 Sense & Sensibility 10 1 1 ## 10 Sense & Sensibility 13 1 the ## # ... with 725,045 more rows Now we have text in a tidy format with one word per row. We filter the “joy” words from NRC lexicon and join them with the words in “Emma” book. nrc_joy <- get_sentiments("nrc") %>% filter(sentiment == "joy") tidy_books %>% filter(book == "Emma") %>% inner_join(nrc_joy) %>% count(word, sort = TRUE) ## Joining, by = "word" ## # A tibble: 303 x 2 ## word n ## <chr> <int> ## 1 good 359 ## 2 young 192 ## 3 friend 166 ## 4 hope 143 ## 5 happy 125 ## 6 love 117 ## 7 deal 92 ## 8 found 92 ## 9 present 89 ## 10 kind 82 ## # ... with 293 more rows Here is another example of using the “bing” lexicon to count the number of positive versus negative words in the different books. # prepare the data b calculating sentiment score library(tidyr) jane_austen_sentiment <- tidy_books %>% inner_join(get_sentiments("bing")) %>% count(book, index = linenumber %/% 80, sentiment) %>% spread(sentiment, n, fill = 0) %>% mutate(sentiment = positive - negative) ## Joining, by = "word" jane_austen_sentiment ## # A tibble: 920 x 5 ## book index negative positive sentiment ## <fct> <dbl> <dbl> <dbl> <dbl> ## 1 Sense & Sensibility 0 16 32 16 ## 2 Sense & Sensibility 1 19 53 34 ## 3 Sense & Sensibility 2 12 31 19 ## 4 Sense & Sensibility 3 15 31 16 ## 5 Sense & Sensibility 4 16 34 18 ## 6 Sense & Sensibility 5 16 51 35 ## 7 Sense & Sensibility 6 24 40 16 ## 8 Sense & Sensibility 7 23 51 28 ## 9 Sense & Sensibility 8 30 40 10 ## 10 Sense & Sensibility 9 15 19 4 ## # ... with 910 more rows # plot the result by book library(ggplot2) ggplot(jane_austen_sentiment, aes(index, sentiment, fill = book)) + geom_col(show.legend = FALSE) + facet_wrap(~book, ncol = 2, scales = "free_x") We can tag positive and negative words by using wordclouds library(reshape2) ## ## Attaching package: 'reshape2' ## The following object is masked from 'package:tidyr': ## ## smiths tidy_books %>% inner_join(get_sentiments("bing")) %>% count(word, sentiment, sort = TRUE) %>% acast(word ~ sentiment, value.var = "n", fill = 0) %>% comparison.cloud(colors = c("red", "green"), max.words = 100) ## Joining, by = "word" 6.3 References: https://www.tidytextmining.com/sentiment.html "],
["word-and-document-frequency-tf-idf.html", "Chapter 7 Word and document frequency (TF-IDF) 7.1 Term frequency application 7.2 Zipf’s law 7.3 TF_IDF metric", " Chapter 7 Word and document frequency (TF-IDF) One major question in text mining and natural langiage procesing is to quantify what a document is about using the words it contains. In addition to measuring “term frequency” metric (tf), we can look at the term’s inverse document frequency (idf). The idf decreases the weight for commonly used words and increases the weight for words that are not used very much in a collection of documents. This metric can be combined with the term frequency to claculate a term’s tf-idf: the frequency of a term adjusted for how rarely is is used. \\(idf(term) = ln (\\frac{n_{documents}}{n_{documents containing term}})\\) 7.1 Term frequency application Let’s count the term frequency in Jane Austen’s novels library(dplyr) library(janeaustenr) library(tidytext) # count term frequency in each book book_words = austen_books() %>% unnest_tokens(word, text) %>% count(book, word, sort = TRUE) # count number of terms in each book total_words = book_words %>% group_by(book) %>% summarize(total = sum(n)) #join both book_words = left_join(book_words, total_words) ## Joining, by = "book" book_words ## # A tibble: 40,379 x 4 ## book word n total ## <fct> <chr> <int> <int> ## 1 Mansfield Park the 6206 160460 ## 2 Mansfield Park to 5475 160460 ## 3 Mansfield Park and 5438 160460 ## 4 Emma to 5239 160996 ## 5 Emma the 5201 160996 ## 6 Emma and 4896 160996 ## 7 Mansfield Park of 4778 160460 ## 8 Pride & Prejudice the 4331 122204 ## 9 Emma of 4291 160996 ## 10 Pride & Prejudice to 4162 122204 ## # ... with 40,369 more rows The resulting table contains one word/book by row: n is the number of times the word is used in a specific book and total is the toal words in the book. Let’s look at the distribution of n/total for each novel. It represents the number of times a word appears iin a novel diveded by the ttal number of terms: the term frequency. library(ggplot2) ggplot(data = book_words, aes(n/total, fill = book)) + geom_histogram(show.legend = FALSE) + xlim(NA, 0.0009) + facet_wrap(~book, ncol = 2, scales = "free_y") ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`. ## Warning: Removed 896 rows containing non-finite values (stat_bin). ## Warning: Removed 6 rows containing missing values (geom_bar). 7.2 Zipf’s law The distribution shon in the previous figure is typical in known as Zipf’s law. It represents the relationships between the frequency of a word and its rank. Zipf’s law states that the frequency that a word appears is inversly proportional to its rank. We can test this hypothesis with Jane Auste’s novels: freq_by_rank = book_words %>% group_by(book) %>% mutate(rank = row_number(), `term frequency` = n/total) freq_by_rank ## # A tibble: 40,379 x 6 ## # Groups: book [6] ## book word n total rank `term frequency` ## <fct> <chr> <int> <int> <int> <dbl> ## 1 Mansfield Park the 6206 160460 1 0.0387 ## 2 Mansfield Park to 5475 160460 2 0.0341 ## 3 Mansfield Park and 5438 160460 3 0.0339 ## 4 Emma to 5239 160996 1 0.0325 ## 5 Emma the 5201 160996 2 0.0323 ## 6 Emma and 4896 160996 3 0.0304 ## 7 Mansfield Park of 4778 160460 4 0.0298 ## 8 Pride & Prejudice the 4331 122204 1 0.0354 ## 9 Emma of 4291 160996 4 0.0267 ## 10 Pride & Prejudice to 4162 122204 2 0.0341 ## # ... with 40,369 more rows In the ibtained dataframe, the rank column represents the rank of rach word within the frequency table (ordered by n). We can visualize the zipf’s law by plotting the rank in the x-axis and term frequency on the y-axis , on logarithmic scales. freq_by_rank %>% ggplot(aes(rank, `term frequency`, color = book)) + geom_line(size = 1.1, alpha = 0.8, show.legend = FALSE) + scale_x_log10() + scale_y_log10() 7.3 TF_IDF metric TF-IDF (Term frequency-inverse document frequency) is a method for evaluating how important a ord is to a document in a collection or corpus. It consists of decreasing the weight for commonly used words and increasing the weight for words that are not used very much in a corpus of documents. \\(w_{term,document} = tf_{term,document} log(\\frac{total number of documents}{number of documents containing the term})\\) Here is an example of measuring Tf-IDF using the bind_tf_idf function book_words <- book_words %>% bind_tf_idf(word, book, n) book_words ## # A tibble: 40,379 x 7 ## book word n total tf idf tf_idf ## <fct> <chr> <int> <int> <dbl> <dbl> <dbl> ## 1 Mansfield Park the 6206 160460 0.0387 0 0 ## 2 Mansfield Park to 5475 160460 0.0341 0 0 ## 3 Mansfield Park and 5438 160460 0.0339 0 0 ## 4 Emma to 5239 160996 0.0325 0 0 ## 5 Emma the 5201 160996 0.0323 0 0 ## 6 Emma and 4896 160996 0.0304 0 0 ## 7 Mansfield Park of 4778 160460 0.0298 0 0 ## 8 Pride & Prejudice the 4331 122204 0.0354 0 0 ## 9 Emma of 4291 160996 0.0267 0 0 ## 10 Pride & Prejudice to 4162 122204 0.0341 0 0 ## # ... with 40,369 more rows We notice that idf and tf-idf scores of commmon words are equivalent to zero since this aproach decreases the weight for common words. The inverse document frequency will be a higher number for words that occur in fewer of the documents in the collection. Let’s look at terms with high tf-idf in Jane Austen’s works book_words %>% select(-total) %>% arrange(desc(tf_idf)) ## # A tibble: 40,379 x 6 ## book word n tf idf tf_idf ## <fct> <chr> <int> <dbl> <dbl> <dbl> ## 1 Sense & Sensibility elinor 623 0.00519 1.79 0.00931 ## 2 Sense & Sensibility marianne 492 0.00410 1.79 0.00735 ## 3 Mansfield Park crawford 493 0.00307 1.79 0.00551 ## 4 Pride & Prejudice darcy 373 0.00305 1.79 0.00547 ## 5 Persuasion elliot 254 0.00304 1.79 0.00544 ## 6 Emma emma 786 0.00488 1.10 0.00536 ## 7 Northanger Abbey tilney 196 0.00252 1.79 0.00452 ## 8 Emma weston 389 0.00242 1.79 0.00433 ## 9 Pride & Prejudice bennet 294 0.00241 1.79 0.00431 ## 10 Persuasion wentworth 191 0.00228 1.79 0.00409 ## # ... with 40,369 more rows Here we see all proper nouns, names that are in fact important in these novels. None of them occur in all of novels, and they are important, characteristic words for each text within the corpus of Jane Austen’s novels. Let’s plot the results: book_words %>% arrange(desc(tf_idf)) %>% mutate(word = factor(word, levels = rev(unique(word)))) %>% group_by(book) %>% top_n(15) %>% ungroup() %>% ggplot(aes(word, tf_idf, fill = book)) + geom_col(show.legend = FALSE) + labs(x = NULL, y = "tf-idf") + facet_wrap(~book, ncol = 2, scales = "free") + coord_flip() ## Selecting by tf_idf "],
["topic-modeling.html", "Chapter 8 Topic modeling 8.1 Latent Dirichlet allocation 8.2 Document-topic probabilities", " Chapter 8 Topic modeling Topic modeling is a type of statistical modeling for discovering the abstract “topics” that occur in a collection of documents. 8.1 Latent Dirichlet allocation Latent Dirichlet allocation (LDA) is an example of topic model and is used to classify text in a document to a particular topic. It treats each document as a mixture of topics, and each topic as a mixture of words. LDA is a mathematical method for finding the mixture of words associated with each topic and the mixture of topics that describes each document. Here is an example of applying LDA model with 2 topics as parameter: library(topicmodels) # load data data("AssociatedPress") AssociatedPress ## <<DocumentTermMatrix (documents: 2246, terms: 10473)>> ## Non-/sparse entries: 302031/23220327 ## Sparsity : 99% ## Maximal term length: 18 ## Weighting : term frequency (tf) # fitting LDA model with 2 topics ap_lda = LDA(AssociatedPress, k=2, control = list(seed = 1234)) ap_lda ## A LDA_VEM topic model with 2 topics. Now we can extract the per-topic-per-word probabilities from the model library(tidytext) ap_topics = tidy(ap_lda, matrix = "beta") ap_topics ## # A tibble: 20,946 x 3 ## topic term beta ## <int> <chr> <dbl> ## 1 1 aaron 1.69e-12 ## 2 2 aaron 3.90e- 5 ## 3 1 abandon 2.65e- 5 ## 4 2 abandon 3.99e- 5 ## 5 1 abandoned 1.39e- 4 ## 6 2 abandoned 5.88e- 5 ## 7 1 abandoning 2.45e-33 ## 8 2 abandoning 2.34e- 5 ## 9 1 abbott 2.13e- 6 ## 10 2 abbott 2.97e- 5 ## # ... with 20,936 more rows The resulting dataframe present the probability of each term to be generated from the different topics. For example the term “abandoned” has a probability of \\(1.39 \\times 10^{-4}\\) of beng generated from topic 1 and a probability of\\(5.88 \\times 10^{-5}\\) for being generated from topic 2. Let’s find the 10 terms that are most common within each topic. library(ggplot2) library(dplyr) ap_top_terms <- ap_topics %>% group_by(topic) %>% top_n(10, beta) %>% ungroup() %>% arrange(topic, -beta) ap_top_terms %>% mutate(term = reorder_within(term, beta, topic)) %>% ggplot(aes(term, beta, fill = factor(topic))) + geom_col(show.legend = FALSE) + facet_wrap(~ topic, scales = "free") + coord_flip() + scale_x_reordered() We can interpret the result as a first topic related to finanial news (“precent’,”million“,”company“) and a second topic related to political news (”president“,”government“,”states\"). 8.2 Document-topic probabilities Besides estimating each topic as a mixture of words, LDA also models each document as a mixture of topics. For examining per-document-per-topic probabilities, we use the “gamma” metric. ap_documents <- tidy(ap_lda, matrix = "gamma") ap_documents ## # A tibble: 4,492 x 3 ## document topic gamma ## <int> <int> <dbl> ## 1 1 1 0.248 ## 2 2 1 0.362 ## 3 3 1 0.527 ## 4 4 1 0.357 ## 5 5 1 0.181 ## 6 6 1 0.000588 ## 7 7 1 0.773 ## 8 8 1 0.00445 ## 9 9 1 0.967 ## 10 10 1 0.147 ## # ... with 4,482 more rows Each of these values is an estimated proportion of words from that document that are generated from that topic. For example, the model estimates that only about 25% of the words in document 1 were generated from topic 1. "],
["words-relationships-analysis.html", "Chapter 9 Words’ relationships analysis 9.1 Extracting bi-grams 9.2 Analyzing bi-grams 9.3 Visualizing a network of bigrams", " Chapter 9 Words’ relationships analysis Some intereseting text analysis techniques consists of quantifying the relationships betwwen words. These analysis help at examining for example which words tend to follow others or to occur within the same documents. 9.1 Extracting bi-grams In order to analyze pairs of words, we can extract the different bi-grams from a corpus of text. library(dplyr) library(tidytext) library(janeaustenr) # extracting bi-grams austen_bigrams = austen_books() %>% unnest_tokens(bigram, text, token = "ngrams", n = 2) austen_bigrams ## # A tibble: 725,049 x 2 ## book bigram ## <fct> <chr> ## 1 Sense & Sensibility sense and ## 2 Sense & Sensibility and sensibility ## 3 Sense & Sensibility sensibility by ## 4 Sense & Sensibility by jane ## 5 Sense & Sensibility jane austen ## 6 Sense & Sensibility austen 1811 ## 7 Sense & Sensibility 1811 chapter ## 8 Sense & Sensibility chapter 1 ## 9 Sense & Sensibility 1 the ## 10 Sense & Sensibility the family ## # ... with 725,039 more rows # counting and filtering bi-grams austen_bigrams %>% count(bigram, sort = TRUE) ## # A tibble: 211,236 x 2 ## bigram n ## <chr> <int> ## 1 of the 3017 ## 2 to be 2787 ## 3 in the 2368 ## 4 it was 1781 ## 5 i am 1545 ## 6 she had 1472 ## 7 of her 1445 ## 8 to the 1387 ## 9 she was 1377 ## 10 had been 1299 ## # ... with 211,226 more rows Since the resulting dataframe contains some stop words, we can attempt to remove them by seperating the bigrams, filtering the stop words and recombinng them after: # seperating bigrams by splitting the "bugram" column library(tidyr) bigrams_separated <- austen_bigrams %>% separate(bigram, c("word1", "word2"), sep = " ") # removing stop words bigrams_filtered <- bigrams_separated %>% filter(!word1 %in% stop_words$word) %>% filter(!word2 %in% stop_words$word) # new bigrams count bigram_counts = bigrams_filtered %>% count(word1, word2, sort = TRUE) bigram_counts ## # A tibble: 33,421 x 3 ## word1 word2 n ## <chr> <chr> <int> ## 1 sir thomas 287 ## 2 miss crawford 215 ## 3 captain wentworth 170 ## 4 miss woodhouse 162 ## 5 frank churchill 132 ## 6 lady russell 118 ## 7 lady bertram 114 ## 8 sir walter 113 ## 9 miss fairfax 109 ## 10 colonel brandon 108 ## # ... with 33,411 more rows # recombing bigrams bigrams_united <- bigrams_filtered %>% unite(bigram, word1, word2, sep = " ") bigrams_united ## # A tibble: 44,784 x 2 ## book bigram ## <fct> <chr> ## 1 Sense & Sensibility jane austen ## 2 Sense & Sensibility austen 1811 ## 3 Sense & Sensibility 1811 chapter ## 4 Sense & Sensibility chapter 1 ## 5 Sense & Sensibility norland park ## 6 Sense & Sensibility surrounding acquaintance ## 7 Sense & Sensibility late owner ## 8 Sense & Sensibility advanced age ## 9 Sense & Sensibility constant companion ## 10 Sense & Sensibility happened ten ## # ... with 44,774 more rows 9.2 Analyzing bi-grams Once we have the list of bi-grams filtered from stop words, we can perform some statistical anaysis by computing for example the TF-IDF values # Measuring the tf-idf values of bigrams bigram_tf_idf = bigrams_united %>% count(book, bigram) %>% bind_tf_idf(bigram, book, n) %>% arrange(desc(tf_idf)) bigram_tf_idf ## # A tibble: 36,217 x 6 ## book bigram n tf idf tf_idf ## <fct> <chr> <int> <dbl> <dbl> <dbl> ## 1 Persuasion captain wentworth 170 0.0299 1.79 0.0535 ## 2 Mansfield Park sir thomas 287 0.0287 1.79 0.0515 ## 3 Mansfield Park miss crawford 215 0.0215 1.79 0.0386 ## 4 Persuasion lady russell 118 0.0207 1.79 0.0371 ## 5 Persuasion sir walter 113 0.0198 1.79 0.0356 ## 6 Emma miss woodhouse 162 0.0170 1.79 0.0305 ## 7 Northanger Abbey miss tilney 82 0.0159 1.79 0.0286 ## 8 Sense & Sensibility colonel brandon 108 0.0150 1.79 0.0269 ## 9 Emma frank churchill 132 0.0139 1.79 0.0248 ## 10 Pride & Prejudice lady catherine 100 0.0138 1.79 0.0247 ## # ... with 36,207 more rows # plotting the results bigram_tf_idf %>% arrange(desc(tf_idf)) %>% mutate(bigram = factor(bigram, levels = rev(unique(bigram)))) %>% group_by(book) %>% top_n(15) %>% ungroup() %>% ggplot(aes(bigram, tf_idf, fill = book)) + geom_col(show.legend = FALSE) + labs(x = NULL, y = "tf-idf") + facet_wrap(~book, ncol = 2, scales = "free") + coord_flip() 9.3 Visualizing a network of bigrams The relationships between words can be visualized as a graph where nodes represent the words and edges represent the bigram connections. In order to make graph visualizing, we will start by transforming our dataframe bigram_counts into a graph. library(igraph) # original dataframe bigram_counts ## # A tibble: 33,421 x 3 ## word1 word2 n ## <chr> <chr> <int> ## 1 sir thomas 287 ## 2 miss crawford 215 ## 3 captain wentworth 170 ## 4 miss woodhouse 162 ## 5 frank churchill 132 ## 6 lady russell 118 ## 7 lady bertram 114 ## 8 sir walter 113 ## 9 miss fairfax 109 ## 10 colonel brandon 108 ## # ... with 33,411 more rows # filter common combinations bigram_graph = bigram_counts %>% filter(n > 20) %>% graph_from_data_frame() bigram_graph ## IGRAPH ab4e2d9 DN-- 91 77 -- ## + attr: name (v/c), n (e/n) ## + edges from ab4e2d9 (vertex names): ## [1] sir ->thomas miss ->crawford captain ->wentworth ## [4] miss ->woodhouse frank ->churchill lady ->russell ## [7] lady ->bertram sir ->walter miss ->fairfax ## [10] colonel ->brandon miss ->bates lady ->catherine ## [13] sir ->john jane ->fairfax miss ->tilney ## [16] lady ->middleton miss ->bingley thousand->pounds ## [19] miss ->dashwood miss ->bennet john ->knightley ## [22] miss ->morland captain ->benwick dear ->miss ## + ... omitted several edges Now we can use the ggraph package in order to make a beautiful visulization of our words graph by specifying the direction of connections. library(ggraph) set.seed(2016) a <- grid::arrow(type = "closed", length = unit(.15, "inches")) ggraph(bigram_graph, layout = "fr") + geom_edge_link(aes(edge_alpha = n), show.legend = FALSE, arrow = a, end_cap = circle(.07, 'inches')) + geom_node_point(color = "lightblue", size = 3) + geom_node_text(aes(label = name), vjust = 1, hjust = 1) + theme_void() "],
["document-term-matrix.html", "Chapter 10 Document-term matrix 10.1 COnverting DTM into dataframe 10.2 Generating Document-term matrix", " Chapter 10 Document-term matrix A document-term matrix is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix: Rows correspond to documents in the collection and Columns correspond to terms Values contain the number of appearances of terms in the specified documents 10.1 COnverting DTM into dataframe We will see how to transform a document-term matrix into a dataframe. We can find examples of DTM data by loading topicmodels package. library(tm) library(topicmodels) library(quanteda) data("AssociatedPress", package = "topicmodels") AssociatedPress ## <<DocumentTermMatrix (documents: 2246, terms: 10473)>> ## Non-/sparse entries: 302031/23220327 ## Sparsity : 99% ## Maximal term length: 18 ## Weighting : term frequency (tf) The loaded dataset contains 2246 documents and 10473 distinct terms. We notice that this DTM is 99% sparse (99% of document-word paris are zero). We can get the terms using Terms() function. terms = Terms(AssociatedPress) head(terms) ## [1] "aaron" "abandon" "abandoned" "abandoning" "abbott" ## [6] "abboud" In order to analyze the data, we should transform it inot dataframe. We can use tidy() function to do that. ap_td = tidy(AssociatedPress) ap_td ## # A tibble: 302,031 x 3 ## document term count ## <int> <chr> <dbl> ## 1 1 adding 1 ## 2 1 adult 2 ## 3 1 ago 1 ## 4 1 alcohol 1 ## 5 1 allegedly 1 ## 6 1 allen 1 ## 7 1 apparently 2 ## 8 1 appeared 1 ## 9 1 arrested 1 ## 10 1 assault 1 ## # ... with 302,021 more rows Once we have the data in a dataframe format, we can perform some analysis. Here is an example of applying sentiment analysis to evaluate the negative and positive terms in the collection. # using "bing" database to attribute negative/positive attribute to terms ap_sentiments = ap_td %>% inner_join(get_sentiments("bing"), by = c(term = "word")) ap_sentiments ## # A tibble: 30,094 x 4 ## document term count sentiment ## <int> <chr> <dbl> <chr> ## 1 1 assault 1 negative ## 2 1 complex 1 negative ## 3 1 death 1 negative ## 4 1 died 1 negative ## 5 1 good 2 positive ## 6 1 illness 1 negative ## 7 1 killed 2 negative ## 8 1 like 2 positive ## 9 1 liked 1 positive ## 10 1 miracle 1 positive ## # ... with 30,084 more rows # plot the results library(ggplot2) ap_sentiments %>% count(sentiment, term, wt = count) %>% ungroup() %>% filter(n >= 200) %>% mutate(n = ifelse(sentiment == "negative", -n, n)) %>% mutate(term = reorder(term, n)) %>% ggplot(aes(term, n, fill = sentiment)) + geom_bar(stat = "identity") + ylab("Contribution to sentiment") + coord_flip() 10.2 Generating Document-term matrix Some algorithms may need document-term matrix as input. The cast_dtm function enable the generation of DTM structure from a dataframe. ap_td %>% cast_dtm(document, term, count) ## <<DocumentTermMatrix (documents: 2246, terms: 10473)>> ## Non-/sparse entries: 302031/23220327 ## Sparsity : 99% ## Maximal term length: 18 ## Weighting : term frequency (tf) We can also generate a Document-feature matrix by using the cast_dfm function ap_td %>% cast_dfm(document, term, count) ## Document-feature matrix of: 2,246 documents, 10,473 features (98.7% sparse). ## features ## docs adding adult ago alcohol allegedly allen apparently appeared arrested ## 1 1 2 1 1 1 1 2 1 1 ## 2 0 0 0 0 0 0 0 1 0 ## 3 0 0 1 0 0 0 0 1 0 ## 4 0 0 3 0 0 0 0 0 0 ## 5 0 0 0 0 0 0 0 0 0 ## 6 0 0 2 0 0 0 0 0 0 ## features ## docs assault ## 1 1 ## 2 0 ## 3 0 ## 4 0 ## 5 0 ## 6 0 ## [ reached max_ndoc ... 2,240 more documents, reached max_nfeat ... 10,463 more features ] "]
]