# 1. Start here

#### Thomas W. Jones

#### 2021-06-27

# Why textmineR?

textmineR was created with three principles in mind:

1. Maximize interoperability within R’s ecosystem
2. Scaleable in terms of object storage and computation time
3. Syntax that is idiomatic to R

R has many packages for text mining and natural language processing (NLP). The CRAN task view on natural language processing lists 53 unique packages. Some of these packages are interoperable. Some are not.

textmineR strives for maximum interoperability in three ways. First, it uses the `dgCMatrix` class from the popular `Matrix` package for document term matrices (DTMs) and term co-occurrence matrices (TCMs). The `Matrix` package is an R “recommended” package with nearly 500 packages that depend, import, or suggest it. Compare that to the `slam` package used by `tm` and its derivatives. `slam` has an order of magnitude fewer dependents. It is simply not as well integrated. `Matrix` also has methods that make the syntax for manipulating its matrices nearly identical to base R. This greatly reduces the cognitive burden of the programmers.

Second, textmineR relies on base R objects for corpus and metadata storage. Actually, it relies on the user to do so. textmineR’s core functions `CreateDtm` and `CreateTcm` take a simple character vector as input. Users may store their corpora as character vectors, lists, or data frames. There is no need to learn a new ‘Corpus’ class.

Third and last, textmineR represents the output of topic models in a consistent way, a list containing two matrices. This is described in more detail in the next section. Several topic models are supported and the simple representation means that textmineR’s utility functions are usable with outputs from other packages, so long as they are represented as matrices of probabilities. (Again, see the next section for more detail.)

textmineR achieves scaleability through three means. First, sparse matrices (like the `dgCMatrix`) offer significant memory savings. Second, textmineR utilizes `Rcpp` throughout for speedup. Finally, textmineR uses parallel processing by default where possible. textmineR offers a function `TmParallelApply` which implements a framework for parallel processing that is syntactically agnostic between Windows and Unix-like operating systems. `TmParallelApply` is used liberally within textmineR and is exposed for users.

textmineR does make some tradeoffs of performance for syntactic simplicity. textmineR is designed to run on a single node in a cluster computing environment. It can (and will by default) use all available cores of that node. If performance is your number one concern, see `text2vec`. textmineR uses some `text2vec` under the hood.

textmineR strives for syntax that is idiomatic to R. This is, admittedly, a nebulous concept. textmineR does not create new classes where existing R classes exist. It strives for a functional programming paradigm. And it attempts to group closely-related sequential steps into single functions. This means that users will not have to make several temporary objects along the way. As an example, compare making a document term matrix in textmineR (example below) with `tm` or `text2vec`.

As a side note: textmineR’s framework for NLP does not need to be exclusive to textmineR. Text mining packages in R can be interoperable with a few concepts. First, use `dgCMatrix` for DTMs and TCMs. Second, write most text mining models in a way that they can take a `dgCMatrix` as the input. Finally, keep non-base R classes to a minimum, especially for corpus and metadata management.

# Corpus management

### Creating a DTM

The basic object of analysis for most text mining applications is a document term matrix, or DTM. This is a matrix where every row represents a document and every column represents a token (word, bi-gram, stem, etc.)

You can create a DTM with textmineR by passing a character vector. There are options for stopword removal, creation of n-grams, and other standard data cleaning. There is an option for passing a stemming or lemmatization function if you desire. (See `help(CreateDtm)` for an example using Porter’s word stemmer.)

The code below uses a dataset of movie reviews included with the `text2vec` package. This dataset is used for sentiment analysis. In addition to the text of the reviews. There is a binary variable indicating positive or negative sentiment. More on this later…

    library(textmineR)
    #> Loading required package: Matrix
    #>
    #> Attaching package: 'textmineR'
    #> The following object is masked from 'package:Matrix':
    #>
    #>     update
    #> The following object is masked from 'package:stats':
    #>
    #>     update

    # load movie_review dataset from text2vec
    data(movie_review, package = "text2vec")

    str(movie_review)
    #> 'data.frame':    5000 obs. of  3 variables:
    #>  $ id       : chr  "5814_8" "2381_9" "7759_3" "3630_4" ...
    #>  $ sentiment: int  1 1 0 0 1 1 0 0 0 1 ...
    #>  $ review   : chr  "With all this stuff going down at the moment with MJ i've started listening to his music, watching the odd docu"| __truncated__ "\\\"The Classic War of the Worlds\\\" by Timothy Hines is a very entertaining film that obviously goes to great"| __truncated__ "The film starts with a manager (Nicholas Bell) giving welcome investors (Robert Carradine) to Primal Park . A s"| __truncated__ "It must be assumed that those who praised this film (\\\"the greatest filmed opera ever,\\\" didn't I read some"| __truncated__ ...

    # let's take a sample so the demo will run quickly
    # note: textmineR is generally quite scaleable, depending on your system
    set.seed(123)
    s <- sample(1:nrow(movie_review), 500)

    movie_review <- movie_review[ s , ]

    # create a document term matrix
    dtm <- CreateDtm(doc_vec = movie_review$review, # character vector of documents
                     doc_names = movie_review$id, # document names, optional
                     ngram_window = c(1, 2), # minimum and maximum n-gram length
                     stopword_vec = c(stopwords::stopwords("en"), # stopwords from tm
                                      stopwords::stopwords(source = "smart")), # this is the default value
                     lower = TRUE, # lowercase - this is the default value
                     remove_punctuation = TRUE, # punctuation - this is the default
                     remove_numbers = TRUE, # numbers - this is the default
                     verbose = FALSE, # Turn off status bar for this demo
                     cpus = 2) # by default, this will be the max number of cpus available

Even though a `dgCMatrix` isn’t a traditional matrix, it has methods that make it similar to standard R matrices.

    dim(dtm)
    #> [1]   500 56023

    nrow(dtm)
    #> [1] 500

    ncol(dtm)
    #> [1] 56023

    head(colnames(dtm))
    #> [1] "aaa"           "aaa_ball"      "aaaaatch"      "aaaaatch_kah"
    #> [5] "aage"          "aage_haugland"

colnames(dtm)
aaa
aaa_ball
aaaaatch
aaaaatch_kah
aage
aage_haugland

    head(rownames(dtm))

rownames(dtm)
4273_1
7112_4
1891_3
6252_10
9929_2
8970_10

# Basic corpus statistics

The code below performs some basic corpus statistics. textmineR has a built in function for getting term frequencies across the corpus. This function `TermDocFreq` gives term frequencies (equivalent to `colSums(dtm)`), the number of documents in which each term appears (equivalent to `colSums(dtm > 0)`), and an inverse-document frequency (IDF) vector. The IDF vector can be used to create a TF-IDF matrix.

    # get counts of tokens across the corpus
    tf_mat <- TermDocFreq(dtm = dtm)

    str(tf_mat)
    #> tibble [56,023 × 4] (S3: tbl_df/tbl/data.frame)
    #>  $ term     : chr [1:56023] "aaa" "aaa_ball" "aaaaatch" "aaaaatch_kah" ...
    #>  $ term_freq: num [1:56023] 1 1 1 1 1 1 1 1 1 1 ...
    #>  $ doc_freq : int [1:56023] 1 1 1 1 1 1 1 1 1 1 ...
    #>  $ idf      : num [1:56023] 6.21 6.21 6.21 6.21 6.21 ...

    # look at the most frequent tokens
    head(tf_mat[ order(tf_mat$term_freq, decreasing = TRUE) , ], 10)

Ten most frequent tokens
term | term_freq | doc_freq | idf
--- | --- | --- | ---
br | 1990 | 289 | 0.5481814
br_br | 999 | 289 | 0.5481814
film | 909 | 294 | 0.5310283
movie | 774 | 290 | 0.5447272
good | 337 | 209 | 0.8722738
time | 252 | 167 | 1.0966143
people | 238 | 143 | 1.2517635
story | 230 | 143 | 1.2517635
great | 181 | 125 | 1.3862944
bad | 176 | 121 | 1.4188176

    # look at the most frequent bigrams
    tf_bigrams <- tf_mat[ stringr::str_detect(tf_mat$term, "_") , ]

    head(tf_bigrams[ order(tf_bigrams$term_freq, decreasing = TRUE) , ], 10)

Ten most frequent bi-grams
term | term_freq | doc_freq | idf
--- | --- | --- | ---
br_br | 999 | 289 | 0.5481814
br_film | 41 | 35 | 2.6592600
br_movie | 34 | 31 | 2.7806209
special_effects | 28 | 24 | 3.0365543
film_br | 20 | 17 | 3.3813948
good_movie | 18 | 17 | 3.3813948
low_budget | 18 | 16 | 3.4420194
waste_time | 17 | 15 | 3.5065579
movie_br | 15 | 15 | 3.5065579
movie_good | 15 | 14 | 3.5755508

It looks like we have stray html tags (“<br>”) in the documents. These aren’t giving us any relevant information about content. (Except, perhaps, that these documents were originally part of web pages.)

The most intuitive approach, perhaps, is to strip these tags from our documents, re-construct a document term matrix, and re-calculate the objects as above. However, a simpler approach would be to simply remove the tokens containing “br” from the DTM we already calculated. This is much more computationally efficient and gives us the same result anyway.

    # remove offending tokens from the DTM
    dtm <- dtm[ , ! stringr::str_detect(colnames(dtm),
                                        "(^br$)|(_br$)|(^br_)") ]

    # re-construct tf_mat and tf_bigrams
    tf_mat <- TermDocFreq(dtm)

    tf_bigrams <- tf_mat[ stringr::str_detect(tf_mat$term, "_") , ]

    head(tf_mat[ order(tf_mat$term_freq, decreasing = TRUE) , ], 10)
    #> # A tibble: 10 x 4
    #>    term      term_freq doc_freq   idf
    #>    <chr>         <dbl>    <int> <dbl>
    #>  1 film            909      294 0.531
    #>  2 movie           774      290 0.545
    #>  3 good            337      209 0.872
    #>  4 time            252      167 1.10
    #>  5 people          238      143 1.25
    #>  6 story           230      143 1.25
    #>  7 great           181      125 1.39
    #>  8 bad             176      121 1.42
    #>  9 made            170      140 1.27
    #> 10 character       169      108 1.53

Ten most frequent terms, ‘<br>’ removed
term | term_freq | doc_freq | idf
--- | --- | --- | ---
film | 909 | 294 | 0.5310283
movie | 774 | 290 | 0.5447272
good | 337 | 209 | 0.8722738
time | 252 | 167 | 1.0966143
people | 238 | 143 | 1.2517635
story | 230 | 143 | 1.2517635
great | 181 | 125 | 1.3862944
bad | 176 | 121 | 1.4188176
made | 170 | 140 | 1.2729657
character | 169 | 108 | 1.5324769

    head(tf_bigrams[ order(tf_bigrams$term_freq, decreasing = TRUE) , ], 10)

Ten most frequent bi-grams, ‘<br>’ removed
term | term_freq | doc_freq | idf
--- | --- | --- | ---
special_effects | 28 | 24 | 3.036554
good_movie | 18 | 17 | 3.381395
low_budget | 18 | 16 | 3.442019
waste_time | 17 | 15 | 3.506558
movie_good | 15 | 14 | 3.575551
real_life | 14 | 11 | 3.816713
watch_movie | 14 | 13 | 3.649659
watching_movie | 14 | 13 | 3.649659
high_school | 13 | 7 | 4.268698
horror_films | 13 | 8 | 4.135167

We can also calculate how many tokens each document contains from the DTM. Note that this reflects the modifications we made in constructing the DTM (removing stop words, punctuation, numbers, etc.).

    # summary of document lengths
    doc_lengths <- rowSums(dtm)

    summary(doc_lengths)
    #>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    #>      20      97     139     188     222     928

Often,it’s useful to prune your vocabulary and remove any tokens that appear in a small number of documents. This will greatly reduce the vocabulary size (see Zipf’s law) and improve computation time.

    # remove any tokens that were in 3 or fewer documents
    dtm <- dtm[ , colSums(dtm > 0) > 3 ] # alternatively: dtm[ , tf_mat$term_freq > 3 ]

    tf_mat <- tf_mat[ tf_mat$term %in% colnames(dtm) , ]

    tf_bigrams <- tf_bigrams[ tf_bigrams$term %in% colnames(dtm) , ]

The movie review data set contains more than just text of reviews. It also contains a variable tagging the review as positive (`movie_review$sentiment` \(=1\)) or negative (`movie_review$sentiment` \(=0\)). We can examine terms associated with positive and negative reviews. If we wanted, we could use them to build a simple classifier.

However, as we will see immediately below, looking at only the most frequent terms in each category is not helpful. Because of Zipf’s law, the most frequent terms in just about any category will be the same.

    # what words are most associated with sentiment?
    tf_sentiment <- list(positive = TermDocFreq(dtm[ movie_review$sentiment == 1 , ]),
                         negative = TermDocFreq(dtm[ movie_review$sentiment == 0 , ]))

These are basically the same. Not helpful at all.

    head(tf_sentiment$positive[ order(tf_sentiment$positive$term_freq, decreasing = TRUE) , ], 10)

Ten most-frequent positive tokens
term | term_freq | doc_freq | idf
--- | --- | --- | ---
film | 483 | 151 | 0.4591837
movie | 340 | 130 | 0.6089291
good | 180 | 106 | 0.8130245
great | 142 | 93 | 0.9438641
time | 138 | 88 | 0.9991267
story | 124 | 75 | 1.1589754
people | 107 | 65 | 1.3020763
films | 102 | 56 | 1.4511119
love | 82 | 53 | 1.5061716
made | 81 | 67 | 1.2717709

    head(tf_sentiment$negative[ order(tf_sentiment$negative$term_freq, decreasing = TRUE) , ], 10)

Ten most-frequent negative tokens
term | term_freq | doc_freq | idf
--- | --- | --- | ---
movie | 434 | 160 | 0.4893466
film | 426 | 143 | 0.6016758
good | 157 | 103 | 0.9297914
people | 131 | 78 | 1.2078116
bad | 130 | 82 | 1.1578012
time | 114 | 79 | 1.1950726
story | 106 | 68 | 1.3450127
character | 95 | 60 | 1.4701758
made | 89 | 73 | 1.2740610
make | 86 | 63 | 1.4213857

That was unhelpful. Instead, we need to re-weight the terms in each class. We’ll use a probabilistic reweighting, described below.

The most frequent words in each class are proportional to \(P(word|sentiment_j)\). As we saw above, that would puts the words in the same order as \(P(word)\), overall. However, we can use the difference in those probabilities to get a new order. That difference is

\[\begin{align} P(word|sentiment_j) - P(word) \end{align}\]

You can interpret the difference in (1) as follows: Positive values are more probable in the sentiment class than in the corpus overall. Negative values are less probable. Values close to zero are statistically-independent of sentiment. Since most of the top words are the same when we sort by \(P(word|sentiment_j)\), these words are statistically-independent of sentiment. They get forced towards zero.

For those paying close attention, this difference should give a similar ordering as pointwise-mutual information (PMI), defined as \(PMI = \frac{P(word|sentiment_j)}{P(word)}\). However, I prefer the difference as it is bound between \(-1\) and \(1\).

The difference method is applied to both words overall and bi-grams in the code below.

    # let's reweight by probability by class
    p_words <- colSums(dtm) / sum(dtm) # alternatively: tf_mat$term_freq / sum(tf_mat$term_freq)

    tf_sentiment$positive$conditional_prob <-
      tf_sentiment$positive$term_freq / sum(tf_sentiment$positive$term_freq)

    tf_sentiment$positive$prob_lift <- tf_sentiment$positive$conditional_prob - p_words

    tf_sentiment$negative$conditional_prob <-
      tf_sentiment$negative$term_freq / sum(tf_sentiment$negative$term_freq)

    tf_sentiment$negative$prob_lift <- tf_sentiment$negative$conditional_prob - p_words

    # let's look again with new weights
    head(tf_sentiment$positive[ order(tf_sentiment$positive$prob_lift, decreasing = TRUE) , ], 10)

Reweighted: ten most relevant terms for positive sentiment
term | term_freq | doc_freq | idf | conditional_prob | prob_lift
--- | --- | --- | --- | --- | ---
great | 142 | 93 | 0.9438641 | 0.0084009 | 0.0029998
film | 483 | 151 | 0.4591837 | 0.0285748 | 0.0014502
love | 82 | 53 | 1.5061716 | 0.0048512 | 0.0012406
films | 102 | 56 | 1.4511119 | 0.0060344 | 0.0010810
performance | 55 | 32 | 2.0107276 | 0.0032539 | 0.0008667
role | 52 | 38 | 1.8388774 | 0.0030764 | 0.0008384
charlie | 28 | 3 | 4.3778513 | 0.0016565 | 0.0007911
excellent | 36 | 31 | 2.0424763 | 0.0021298 | 0.0006975
performances | 34 | 21 | 2.4319411 | 0.0020115 | 0.0006687
time | 138 | 88 | 0.9991267 | 0.0081642 | 0.0006445

    head(tf_sentiment$negative[ order(tf_sentiment$negative$prob_lift, decreasing = TRUE) , ], 10)

Reweighted: ten most relevant terms for negative sentiment
term | term_freq | doc_freq | idf | conditional_prob | prob_lift
--- | --- | --- | --- | --- | ---
movie | 434 | 160 | 0.4893466 | 0.0261304 | 0.0030342
bad | 130 | 82 | 1.1578012 | 0.0078271 | 0.0025752
worst | 47 | 39 | 1.9009588 | 0.0028298 | 0.0012483
pretty | 54 | 41 | 1.8509483 | 0.0032512 | 0.0010729
poor | 42 | 34 | 2.0381599 | 0.0025287 | 0.0009771
guy | 56 | 28 | 2.2323159 | 0.0033717 | 0.0009248
part | 57 | 43 | 1.8033203 | 0.0034319 | 0.0008955
black | 41 | 20 | 2.5687881 | 0.0024685 | 0.0008870
minutes | 44 | 32 | 2.0987845 | 0.0026492 | 0.0008588
stupid | 31 | 24 | 2.3864666 | 0.0018665 | 0.0008519

    # what about bi-grams?
    tf_sentiment_bigram <- lapply(tf_sentiment, function(x){
      x <- x[ stringr::str_detect(x$term, "_") , ]
      x[ order(x$prob_lift, decreasing = TRUE) , ]
    })

    head(tf_sentiment_bigram$positive, 10)

Reweighted: ten most relevant bigrams for positive sentiment
term | term_freq | doc_freq | idf | conditional_prob | prob_lift
--- | --- | --- | --- | --- | ---
makes_movie | 10 | 8 | 3.397022 | 0.0005916 | 0.0002932
good_movie | 14 | 14 | 2.837406 | 0.0008283 | 0.0002911
high_school | 11 | 5 | 3.867026 | 0.0006508 | 0.0002629
star_wars | 9 | 5 | 3.867026 | 0.0005324 | 0.0002340
great_movie | 8 | 8 | 3.397022 | 0.0004733 | 0.0002047
highly_recommended | 6 | 6 | 3.684704 | 0.0003550 | 0.0001759
great_film | 8 | 7 | 3.530553 | 0.0004733 | 0.0001749
movie_great | 8 | 7 | 3.530553 | 0.0004733 | 0.0001749
academy_award | 5 | 4 | 4.090169 | 0.0002958 | 0.0001466
acting_great | 5 | 5 | 3.867026 | 0.0002958 | 0.0001466

    head(tf_sentiment_bigram$negative, 10)

Reweighted: ten most relevant bigrams for negative sentiment
term | term_freq | doc_freq | idf | conditional_prob | prob_lift
--- | --- | --- | --- | --- | ---
waste_time | 16 | 14 | 2.925463 | 0.0009633 | 0.0004561
special_effects | 20 | 16 | 2.791932 | 0.0012042 | 0.0003686
movie_made | 10 | 10 | 3.261935 | 0.0006021 | 0.0002440
make_sense | 9 | 6 | 3.772761 | 0.0005419 | 0.0002136
scooby_doo | 8 | 4 | 4.178226 | 0.0004817 | 0.0002131
worst_movie | 7 | 6 | 3.772761 | 0.0004215 | 0.0002126
part_movie | 8 | 6 | 3.772761 | 0.0004817 | 0.0001833
read_book | 8 | 8 | 3.485079 | 0.0004817 | 0.0001833
good_idea | 6 | 6 | 3.772761 | 0.0003612 | 0.0001822
main_character | 8 | 6 | 3.772761 | 0.0004817 | 0.0001534