#

1. Start here

#### Thomas W. Jones

#### 2018-03-06

# Why textmineR?

textmineR was created with three principles in mind:

1. Maximize interoperability within R’s ecosystem
2. Scaleable in terms of object storeage 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-occurence 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 useable with outputs from other packages, so long as they are represented as matrices of probabilities. (Again, see the next section for more detail.)

textmineR acheives scalability 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

    # 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(tm::stopwords("english"), # stopwords from tm
                                      tm::stopwords("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 55459

    nrow(dtm)
    #> [1] 500

    ncol(dtm)
    #> [1] 55459

    head(colnames(dtm))
    #> [1] "making_debut"       "bureau_lowly"       "scenes_thing"
    #> [4] "injections"         "frying_pan"         "renounced_assassin"

colnames(dtm)
making_debut
bureau_lowly
scenes_thing
injections
frying_pan
renounced_assassin

    head(rownames(dtm))

rownames(dtm)
2595_9
8892_2
8620_8
2892_10
232_1
4364_1

# 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)
    #> 'data.frame':    55459 obs. of  4 variables:
    #>  $ term     : chr  "making_debut" "bureau_lowly" "scenes_thing" "injections" ...
    #>  $ term_freq: num  1 1 1 1 1 1 1 1 1 1 ...
    #>  $ doc_freq : int  1 1 1 1 1 1 1 1 1 1 ...
    #>  $ idf      : num  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 | br | 2148 | 312 | 0.4716049
br_br | br_br | 1078 | 312 | 0.4716049
movie | movie | 878 | 310 | 0.4780358
film | film | 835 | 284 | 0.5656339
good | good | 333 | 203 | 0.9014021
story | story | 277 | 167 | 1.0966143
time | time | 271 | 180 | 1.0216512
bad | bad | 199 | 118 | 1.4439235
great | great | 195 | 138 | 1.2873544
made | made | 173 | 137 | 1.2946272

    # 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 | br_br | 1078 | 312 | 0.4716049
br_film | br_film | 48 | 41 | 2.5010360
br_movie | br_movie | 41 | 36 | 2.6310892
film_br | film_br | 32 | 26 | 2.9565116
movie_br | movie_br | 29 | 27 | 2.9187712
special_effects | special_effects | 21 | 19 | 3.2701691
good_movie | good_movie | 16 | 15 | 3.5065579
long_time | long_time | 15 | 15 | 3.5065579
high_school | high_school | 15 | 10 | 3.9120230
scooby_doo | scooby_doo | 15 | 1 | 6.2146081

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)
    #>        term term_freq doc_freq       idf
    #> movie movie       878      310 0.4780358
    #> film   film       835      284 0.5656339
    #> good   good       333      203 0.9014021
    #> story story       277      167 1.0966143
    #> time   time       271      180 1.0216512
    #> bad     bad       199      118 1.4439235
    #> great great       195      138 1.2873544
    #> made   made       173      137 1.2946272
    #> watch watch       158      127 1.3704210
    #> films films       153       93 1.6820086

Ten most frequent terms, ‘<br>’ removed
| term | term_freq | doc_freq | idf
--- | --- | --- | --- | ---
movie | movie | 878 | 310 | 0.4780358
film | film | 835 | 284 | 0.5656339
good | good | 333 | 203 | 0.9014021
story | story | 277 | 167 | 1.0966143
time | time | 271 | 180 | 1.0216512
bad | bad | 199 | 118 | 1.4439235
great | great | 195 | 138 | 1.2873544
made | made | 173 | 137 | 1.2946272
watch | watch | 158 | 127 | 1.3704210
films | films | 153 | 93 | 1.6820086

    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 | special_effects | 21 | 19 | 3.270169
good_movie | good_movie | 16 | 15 | 3.506558
long_time | long_time | 15 | 15 | 3.506558
high_school | high_school | 15 | 10 | 3.912023
scooby_doo | scooby_doo | 15 | 1 | 6.214608
low_budget | low_budget | 15 | 13 | 3.649659
watch_movie | watch_movie | 14 | 13 | 3.649659
make_film | make_film | 14 | 13 | 3.649659
years_ago | years_ago | 14 | 13 | 3.649659
movie_good | movie_good | 13 | 13 | 3.649659

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.
    #>    23.0    96.0   140.5   186.1   245.0   768.0

Often,it’s useful to prune your vocabulary and remove any tokens that appear in a samall 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
--- | --- | --- | --- | ---
movie | movie | 358 | 128 | 0.5990082
film | film | 349 | 125 | 0.6227247
story | story | 143 | 82 | 1.0443192
good | good | 138 | 83 | 1.0321978
time | time | 125 | 82 | 1.0443192
great | great | 119 | 79 | 1.0815906
watch | watch | 82 | 59 | 1.3735010
love | love | 71 | 49 | 1.5592182
life | life | 69 | 49 | 1.5592182
character | character | 69 | 53 | 1.4807465

    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 | movie | 520 | 182 | 0.3832420
film | film | 486 | 159 | 0.5183445
good | good | 195 | 120 | 0.7997569
bad | bad | 164 | 90 | 1.0874390
time | time | 146 | 98 | 1.0022812
story | story | 134 | 85 | 1.1445974
made | made | 111 | 83 | 1.1684081
people | people | 104 | 68 | 1.3677410
acting | acting | 102 | 79 | 1.2178008
make | make | 89 | 70 | 1.3387534

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 | great | 119 | 79 | 1.081591 | 0.0081168 | 0.0022971
heart | heart | 42 | 17 | 2.617825 | 0.0028647 | 0.0015217
story | story | 143 | 82 | 1.044319 | 0.0097538 | 0.0014868
life | life | 69 | 49 | 1.559218 | 0.0047064 | 0.0012444
excellent | excellent | 38 | 33 | 1.954531 | 0.0025919 | 0.0012191
beautiful | beautiful | 39 | 28 | 2.118834 | 0.0026601 | 0.0011977
find | find | 51 | 41 | 1.737466 | 0.0034786 | 0.0009418
world | world | 49 | 38 | 1.813452 | 0.0033422 | 0.0008950
watch | watch | 82 | 59 | 1.373501 | 0.0055931 | 0.0008776
years | years | 60 | 43 | 1.689838 | 0.0040925 | 0.0008693

    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
--- | --- | --- | --- | --- | --- | ---
bad | bad | 164 | 90 | 1.0874390 | 0.0087021 | 0.0027631
movie | movie | 520 | 182 | 0.3832420 | 0.0275921 | 0.0013886
people | people | 104 | 68 | 1.3677410 | 0.0055184 | 0.0011313
worst | worst | 55 | 48 | 1.7160476 | 0.0029184 | 0.0011277
script | script | 62 | 50 | 1.6752257 | 0.0032898 | 0.0009023
acting | acting | 102 | 79 | 1.2178008 | 0.0054123 | 0.0008759
film | film | 486 | 159 | 0.5183445 | 0.0257880 | 0.0008678
guy | guy | 55 | 33 | 2.0907411 | 0.0029184 | 0.0008591
thing | thing | 66 | 56 | 1.5618970 | 0.0035021 | 0.0007564
awful | awful | 35 | 24 | 2.4091948 | 0.0018572 | 0.0007529

    # 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
--- | --- | --- | --- | --- | --- | ---
highly_recommend | highly_recommend | 11 | 11 | 3.053143 | 0.0007503 | 0.0003922
big_screen | big_screen | 8 | 5 | 3.841601 | 0.0005457 | 0.0002771
real_life | real_life | 9 | 8 | 3.371597 | 0.0006139 | 0.0002557
world_war | world_war | 8 | 5 | 3.841601 | 0.0005457 | 0.0002174
watched_movie | watched_movie | 7 | 7 | 3.505128 | 0.0004775 | 0.0002089
enjoy_watching | enjoy_watching | 6 | 6 | 3.659279 | 0.0004092 | 0.0002003
years_ago | years_ago | 9 | 8 | 3.371597 | 0.0006139 | 0.0001961
makes_movie | makes_movie | 5 | 5 | 3.841601 | 0.0003410 | 0.0001918
loved_movie | loved_movie | 5 | 5 | 3.841601 | 0.0003410 | 0.0001918
movie_worth | movie_worth | 6 | 6 | 3.659279 | 0.0004092 | 0.0001705

    head(tf_sentiment_bigram$negative, 10)

Reweighted: ten most relevant bigrams for negative sentiment
| term | term_freq | doc_freq | idf | conditional_prob | prob_lift
--- | --- | --- | --- | --- | --- | ---
good_thing | good_thing | 11 | 11 | 3.189353 | 0.0005837 | 0.0002554
waste_time | waste_time | 12 | 11 | 3.189353 | 0.0006367 | 0.0002488
acting_bad | acting_bad | 10 | 9 | 3.390024 | 0.0005306 | 0.0002322
bad_acting | bad_acting | 9 | 9 | 3.390024 | 0.0004776 | 0.0002090
worst_movie | worst_movie | 10 | 10 | 3.284664 | 0.0005306 | 0.0002023
read_book | read_book | 8 | 6 | 3.795489 | 0.0004245 | 0.0001857
comic_book | comic_book | 8 | 6 | 3.795489 | 0.0004245 | 0.0001857
great_idea | great_idea | 7 | 7 | 3.641338 | 0.0003714 | 0.0001625
bad_guys | bad_guys | 8 | 6 | 3.795489 | 0.0004245 | 0.0001559
make_sense | make_sense | 8 | 6 | 3.795489 | 0.0004245 | 0.0001559