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README.md

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sentimentr is designed to quickly calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).

sentimentr is a response to my own needs with sentiment detection that were not addressed by the current R tools. My own polarity function in the qdap package is slower on larger data sets. It is a dictionary lookup approach that tries to incorporate weighting for valence shifters (negation and amplifiers/deamplifiers). Matthew Jocker's created the syuzhet package that utilizes dictionary lookups for the Bing, NRC, and Afinn methods. He also utilizes a wrapper for the Stanford coreNLP which uses much more sophisticated analysis. Jocker's dictionary methods are fast but are more prone to error in the case of valence shifters. Jocker's addressed these critiques explaining that the method is good with regard to analyzing general sentiment in a piece of literature. He points to the accuracy of the Stanford detection as well. In my own work I need better accuracy than a simple dictionary lookup; something that considers valence shifters yet optimizes speed which the Stanford's parser does not. This leads to a trade off of speed vs. accuracy. The equation below describes the dictionary method of sentimentr that may give better results than a dictionary approach that does not consider valence shifters but will likely still be less accurate than Stanford's approach. Simply, sentimentr attempts to balance accuracy and speed.

Table of Contents

Functions

There are two main functions (top 2 in table below) in sentimentr with several helper functions summarized in the table below:

Function Description
sentiment Sentiment at the sentence level
sentiment_by Aggregated sentiment by group(s)
uncombine Extract sentence level sentiment from sentiment_by
get_sentences Regex based string to sentence parser (or get sentences from sentiment/sentiment_by)
replace_emoticon Replace emoticons with word equivalent
replace_grade Replace grades (e.g., "A+") with word equivalent
replace_rating Replace ratings (e.g., "10 out of 10", "3 stars") with word equivalent
as_key Coerce a data.frame lexicon to a polarity hash key
is_key Check if an object is a hash key
update_key Add/remove terms to/from a hash key
highlight Highlight positive/negative sentences as an HTML document

The Equation

The equation used by the algorithm to assign value to polarity of each sentence fist utilizes the sentiment dictionary (Hu and Liu, 2004) to tag polarized words. Each paragraph (pi = {s1, s2, ..., sn}) composed of sentences, is broken into element sentences (si, j = {w1, w2, ..., wn}) where w are the words within sentences. Each sentence (sj) is broken into a an ordered bag of words. Punctuation is removed with the exception of pause punctuations (commas, colons, semicolons) which are considered a word within the sentence. I will denote pause words as cw (comma words) for convenience. We can represent these words as an i,j,k notation as wi, j, k. For example w3, 2, 5 would be the fifth word of the second sentence of the third paragraph. While I use the term paragraph this merely represent a complete turn of talk. For example it may be a cell level response in a questionnaire composed of sentences.

The words in each sentence (wi, j, k) are searched and compared to a modified version of Hu, M., & Liu, B.'s (2004) dictionary of polarized words. Positive (wi, j, k+) and negative (wi, j, k) words are tagged with a +1 and −1 respectively (or other positive/negative weighting if the user provides the sentiment dictionary). I will denote polarized words as pw for convenience. These will form a polar cluster (ci, j, l) which is a subset of the a sentence (ci, j, l ⊆ si, j).

The polarized context cluster (ci, j, l) of words is pulled from around the polarized word (pw) and defaults to 4 words before and two words after pw to be considered as valence shifters. The cluster can be represented as (ci, j, l = {pwi, j, k − nb, ..., pwi, j, k, ..., pwi, j, k − na}), where nb & na are the parameters n.before and n.after set by the user. The words in this polarized context cluster are tagged as neutral (wi, j, k0), negator (wi, j, kn), amplifier (wi, j, ka), or de-amplifier (wi, j, kd). Neutral words hold no value in the equation but do affect word count (n). Each polarized word is then weighted (w) based on the weights from the polarity_dt argument and then further weighted by the function and number of the valence shifters directly surrounding the positive or negative word (pw). Pause (cw) locations (punctuation that denotes a pause including commas, colons, and semicolons) are indexed and considered in calculating the upper and lower bounds in the polarized context cluster. This is because these marks indicate a change in thought and words prior are not necessarily connected with words after these punctuation marks. The lower bound of the polarized context cluster is constrained to max{pwi, j, k − nb, 1, max{cwi, j, k < pwi, j, k}} and the upper bound is constrained to min{pwi, j, k + na, wi, jn, min{cwi, j, k > pwi, j, k}} where wi, jn is the number of words in the sentence.

The core value in the cluster, the polarized word is acted upon by valence shifters. Amplifiers increase the polarity by 1.8 (.8 is the default weight (z)). Amplifiers (wi, j, ka) become de-amplifiers if the context cluster contains an odd number of negators (wi, j, kn). De-amplifiers work to decrease the polarity. Negation (wi, j, kn) acts on amplifiers/de-amplifiers as discussed but also flip the sign of the polarized word. Negation is determined by raising −1 to the power of the number of negators (wi, j, kn) plus 2. Simply, this is a result of a belief that two negatives equal a positive, 3 negatives a negative, and so on.

The "but" conjunctions (i.e., 'but', 'however', and 'although') also weight the context cluster. A but conjunction before the polarized word (wbutconjunction, ..., wi, j, kp) up-weights the cluster by 1 + z2 * {|wbutconjunction|,...,wi, j, kp} (.85 is the default weight (z2) where |wbutconjunction| are the number of but conjunctions before the polarized word). A but conjunction after the polarized word down-weights the cluster by 1 + {wi, j, kp, ..., |wbutconjunction|* − 1}*z2. This corresponds to the belief that a but makes the next clause of greater values while lowering the value placed on the prior clause.

The researcher may provide a weight (z) to be utilized with amplifiers/de-amplifiers (default is .8; de-amplifier weight is constrained to −1 lower bound). Last, these weighted context clusters (ci, j, l) are summed (ci, j) and divided by the square root of the word count (√wi, jn) yielding an unbounded polarity score (δi, j) for each sentence.

δij = c'ij/√wijn

Where:

ci, j = ∑((1 + wamp + wdeamp)⋅wi, j, kp(−1)2 + wneg)

wamp = ∑(wneg ⋅ (z ⋅ wi, j, ka))

wdeamp = max(wdeamp, −1)

wdeamp = ∑(z(−wneg ⋅ wi, j, ka + wi, j, kd))

wb = 1 + z2 * wb

wb = ∑(|wbutconjunction|,...,wi, j, kp, wi, j, kp, ..., |wbutconjunction|* − 1)

wneg = (∑wi, j, kn ) mod 2

To get the mean of all sentences (si, j) within a paragraph (pi) simply take the average sentiment score pi, δi, j = 1/n ⋅ ∑ δi, j.

Installation

To download the development version of sentimentr:

Download the zip ball or tar ball, decompress and run R CMD INSTALL on it, or use the pacman package to install the development version:

if (!require("pacman")) install.packages("pacman")
pacman::p_load_current_gh("trinker/lexicon", "trinker/sentimentr")

Examples

if (!require("pacman")) install.packages("pacman")
pacman::p_load(sentimentr)

mytext <- c(
    'do you like it?  But I hate really bad dogs',
    'I am the best friend.',
    'Do you really like it?  I\'m not a fan'
)
sentiment(mytext)

##    element_id sentence_id word_count  sentiment
## 1:          1           1          4  0.5000000
## 2:          1           2          6 -2.6781088
## 3:          2           1          5  0.4472136
## 4:          3           1          5  0.8049845
## 5:          3           2          4  0.0000000

To aggregate by element (column cell or vector element) use sentiment_by with by = NULL.

mytext <- c(
    'do you like it?  But I hate really bad dogs',
    'I am the best friend.',
    'Do you really like it?  I\'m not a fan'
)
sentiment_by(mytext)

##    element_id word_count       sd ave_sentiment
## 1:          1         10 2.247262    -1.0890544
## 2:          2          5       NA     0.4472136
## 3:          3          9 0.569210     0.4392690

To aggregate by grouping variables use sentiment_by using the by argument.

(out <- with(presidential_debates_2012, sentiment_by(dialogue, list(person, time))))

##        person   time word_count        sd ave_sentiment
##  1:     OBAMA time 1       3598 0.4489512    0.17963450
##  2:     OBAMA time 2       7476 0.3878883    0.21629706
##  3:     OBAMA time 3       7241 0.4408708    0.19103322
##  4:    ROMNEY time 1       4085 0.3669465    0.11715169
##  5:    ROMNEY time 2       7534 0.3271200    0.10396263
##  6:    ROMNEY time 3       8302 0.3866709    0.13649872
##  7:   CROWLEY time 2       1672 0.2356299    0.24872950
##  8:    LEHRER time 1        765 0.3634981    0.32699539
##  9:  QUESTION time 2        583 0.3282897    0.04967577
## 10: SCHIEFFER time 3       1445 0.3810998    0.16056579

Plotting

Plotting at Aggregated Sentiment

plot(out)

Plotting at the Sentence Level

The plot method for the class sentiment uses syuzhet's get_transformed_values combined with ggplot2 to make a reasonable, smoothed plot for the duration of the text based on percentage, allowing for comparison between plots of different texts. This plot gives the overall shape of the text's sentiment. The user can see syuzhet::get_transformed_values for more details.

plot(uncombine(out))

Making and Updating Dictionaries

It is pretty straight forward to make or update a new dictionary (polarity or valence shifter). To create a key from scratch the user needs to create a 2 column data.frame, with words on the left and values on the right (see ?lexicon::hash_sentiment & ?lexicon::hash_valence_shifters for what the values mean). Note that the words need to be lower cased. Here I show an example data.frame ready for key conversion:

set.seed(10)
key <- data.frame(
    words = sample(letters),
    polarity = rnorm(26),
    stringsAsFactors = FALSE
)

This is not yet a key. sentimentr provides the is_key function to test if a table is a key.

is_key(key)

## [1] FALSE

It still needs to be data.table-ified. The as_key function coerces a data.frame to a data.table with the left column named x and the right column named y. It also checks the key against another key to make sure there is not overlap using the compare argument. By default as_key checks against valence_shifters_table, assuming the user is creating a sentiment dictionary. If the user is creating a valence shifter key then a sentiment key needs to be passed to compare instead and set the argument sentiment = FALSE. Below I coerce key to a dictionary that sentimentr can use.

mykey <- as_key(key)

Now we can check that mykey is a usable dictionary:

is_key(mykey)

## [1] TRUE

The key is ready for use:

sentiment_by("I am a human.", polarity_dt = mykey)

##    element_id word_count sd ave_sentiment
## 1:          1          4 NA    -0.7594893

You can see the values of a key that correspond to a word using data.table syntax:

mykey[c("a", "b")][[2]]

## [1] -0.2537805 -0.1951504

Updating (adding or removing terms) a key is also useful. The update_key function allows the user to add or drop terms via the x (add a data.frame) and drop (drop a term) arguments. Below I drop the "a" and "h" terms (notice there are now 24 rows rather than 26):

mykey_dropped <- update_key(mykey, drop = c("a", "h"))
nrow(mykey_dropped)

## [1] 24

sentiment_by("I am a human.", polarity_dt = mykey_dropped)

##    element_id word_count sd ave_sentiment
## 1:          1          4 NA     -0.632599

Next I add the terms "dog" and "cat" as a data.frame with sentiment values:

mykey_added <- update_key(mykey, x = data.frame(x = c("dog", "cat"), y = c(1, -1)))

## Warning in as_key(x, comparison = comparison, sentiment = sentiment): Column 1 was a factor...
## Converting to character.

nrow(mykey_added)

## [1] 28

sentiment("I am a human. The dog.  The cat", polarity_dt = mykey_added)

##    element_id sentence_id word_count  sentiment
## 1:          1           1          4 -0.7594893
## 2:          1           2          2  0.7071068
## 3:          1           3          2 -0.7071068

Annie Swafford's Examples

Annie Swafford critiqued Jocker's approach to sentiment and gave the following examples of sentences (ase for Annie Swafford example). Here I test each of Jocker's 3 dictionary approaches (Bing, NRC, Afinn), his Stanford wrapper (note I use my own GitHub Stanford wrapper package based off of Jocker's approach as it works more reliably on my own Windows machine), the RSentiment package, and my own algorithm with both the default Hu & Liu (2004) polarity lexicon as well as Baccianella, Esuli and Sebastiani's (2010) SentiWord lexicon from the lexicon package.

if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/sentimentr", "trinker/stansent")
pacman::p_load(syuzhet, qdap, microbenchmark, RSentiment)

ase <- c(
    "I haven't been sad in a long time.",
    "I am extremely happy today.",
    "It's a good day.",
    "But suddenly I'm only a little bit happy.",
    "Then I'm not happy at all.",
    "In fact, I am now the least happy person on the planet.",
    "There is no happiness left in me.",
    "Wait, it's returned!",
    "I don't feel so bad after all!"
)

syuzhet <- setNames(as.data.frame(lapply(c("bing", "afinn", "nrc"),
    function(x) get_sentiment(ase, method=x))), c("bing", "afinn", "nrc"))


left_just(data.frame(
    stanford = sentiment_stanford(ase)[["sentiment"]],
    hu_liu = round(sentiment(ase, question.weight = 0)[["sentiment"]], 2),
    sentiword = round(sentiment(ase, lexicon::hash_sentiword, question.weight = 0)[["sentiment"]], 2),    
    RSentiment = calculate_score(ase), 
    syuzhet,
    sentences = ase,
    stringsAsFactors = FALSE
), "sentences")

  stanford hu_liu sentiword RSentiment bing afinn nrc
1     -0.5   0.35      0.18         -1   -1    -2   0
2        1    0.8      0.65          1    1     3   1
3      0.5    0.5      0.32          1    1     3   1
4     -0.5      0         0          0    1     3   1
5     -0.5  -0.41     -0.56         -1    1     3   1
6     -0.5   0.06      0.11          1    1     3   1
7     -0.5  -0.38     -0.05         -1    1     2   1
8        0      0     -0.14          0    0     0  -1
9     -0.5   0.38      0.24         -1   -1    -3  -1
  sentences                                              
1 I haven't been sad in a long time.                     
2 I am extremely happy today.                            
3 It's a good day.                                       
4 But suddenly I'm only a little bit happy.              
5 Then I'm not happy at all.                             
6 In fact, I am now the least happy person on the planet.
7 There is no happiness left in me.                      
8 Wait, it's returned!                                   
9 I don't feel so bad after all!                         

Also of interest is the computational time used by each of these methods. To demonstrate this I increased Annie's examples by 100 replications and microbenchmark on a few iterations (Stanford takes so long I didn't extend to more). Note that if a text needs to be broken into sentence parts syuzhet has the get_sentences function that uses the openNLP package, this is a time expensive task. sentimentr uses a much faster regex based approach that is nearly as accurate in parsing sentences with a much lower computational time. We see that Stanford takes the longest time while sentimentr and syuzhet are comparable depending upon lexicon used. RSentiment is a bit slower than the fastest versions of either sentimentr or syuzhet.

ase_100 <- rep(ase, 100)

stanford <- function() {sentiment_stanford(ase_100)}

sentimentr_hu_liu <- function() sentiment(ase_100)
sentimentr_sentiword <- function() sentiment(ase_100, lexicon::hash_sentiword) 

RSentiment <- function() calculate_score(ase_100) 

syuzhet_binn <- function() get_sentiment(ase_100, method="bing")
syuzhet_nrc <- function() get_sentiment(ase_100, method="nrc")
syuzhet_afinn <- function() get_sentiment(ase_100, method="afinn")

microbenchmark(
    stanford(),
    sentimentr_hu_liu(),
    sentimentr_sentiword(),
    RSentiment(), 
    syuzhet_binn(), 
    syuzhet_nrc(),
    syuzhet_afinn(),
    times = 3
)

Unit: milliseconds
                   expr        min         lq       mean     median
             stanford() 26401.7466 26787.5156 27360.8073 27173.2847
    sentimentr_hu_liu()   270.7224   279.0113   281.9663   287.3003
 sentimentr_sentiword()   971.2202   988.4731  1000.2441  1005.7259
           RSentiment()   706.5924   719.5848   757.7893   732.5772
         syuzhet_binn()   393.1390   394.5057   432.2248   395.8724
          syuzhet_nrc()   982.4704   985.5732   990.2867   988.6759
        syuzhet_afinn()   186.3573   188.2666   223.8937   190.1758
         uq        max neval cld
 27840.3377 28507.3907     3   b
   287.5883   287.8763     3  a 
  1014.7561  1023.7863     3  a 
   783.3878   834.1984     3  a 
   451.7677   507.6629     3  a 
   994.1949   999.7138     3  a 
   242.6619   295.1479     3  a 

Comparing sentimentr, syuzhet, RSentiment, and Stanford

The accuracy of an algorithm weighs heavily into the decision as to what approach to take in sentiment detection. Both syuzhet and sentimentr provide multiple dictionaries with a general algorithm to compute sentiment scores. syuzhet provides 3 approaches while sentimentr provides 2, but can be extended easily using the 3 dictionaries from the syuzhet package. The follow visualization provides the accuracy of these approaches in comparison to Stanford's Java based implementation of sentiment detection. The visualization is generated from testing on three reviews data sets from Kotzias, Denil, De Freitas, & Smyth (2015). These authors utilized the three 1000 element data sets from:

  • amazon.com
  • imdb.com
  • yelp.com

The data sets are hand scored as either positive or negative. The testing here merely matches the sign of the algorithm to the human coded output to determine accuracy rates.

sent comp

The bar graph on the left shows the accuracy rates for the various sentiment set-ups in the three review contexts. The rank plot on the right shows how the rankings for the methods varied across the three review contexts.

The take away here seems that, unsurprisingly, Stanford's algorithm consistently outscores sentimentr, syuzhet, and RSentiment. The sentimentr approach loaded with the hu_lu dictionary is a top pick for speed and accuracy. The bing dictionary also performs well within both the syuzhet and sentimentr algorithms. Generally, the sentimentr algorithm out performs syuzhet when their dictonaries are comparable.

It is important to point out that this is a small sample data set that covers a narrow range of uses for sentiment detection. Jocker's syuzhet was designed to be applied across book chunks and it is, to some extent, unfair to test it out of this context. Still this initial analysis provides a guide that may be of use for selecting the sentiment detection set up most applicable to the reader's needs.

The reader may access the R script used to generate this visual via:

testing <- system.file("sentiment_testing/sentiment_testing.R", package = "sentimentr")
file.copy(testing, getwd())

In the figure below we compare raw table counts as a heat map, plotting the predicted values from the various algorithms on the x axis versus the human scored values on the y axis.

sent comp

Across all three contexts, notice that the Stanford coreNLP algorithm is better at:

  • Detecting negative sentiment as negative
  • Discrimination (i.e., reducing neutral assignments)

The Bing, Hu & Lu, and Afinn dictionaries all do well with regard to not assigning negative scores to positive statements, but perform less well in the reverse, often assigning positive scores to negative statements. We can now see that the reason for the NRC's poorer performance in accuracy rate above is its inability to discriminate. The Sentiword dictionary does well at discriminating (like Stanford's coreNLP) but lacks accuracy. We can deduce two things from this observation:

  1. Larger dictionaries discriminate better (Sentiword [n = 20,100] vs. Hu & Lu [n = 6,875])
  2. The Sentiword dictionary may have words with reversed polarities

A reworking of the Sentiword dictionary may yield better results for a dictionary lookup approach to sentiment detection, potentially, improving on discrimination and accuracy.

The reader may access the R script used to generate this visual via:

testing2 <- system.file("sentiment_testing/raw_results.R", package = "sentimentr")
file.copy(testing2, getwd())

Text Highlighting

The user may wish to see the output from sentiment_by line by line with positive/negative sentences highlighted. The highlight function wraps a sentiment_by output to produces a highlighted HTML file (positive = green; negative = pink). Here we look at three random reviews from Hu and Liu's (2004) Cannon G3 Camera Amazon product reviews.

set.seed(2)
highlight(with(subset(cannon_reviews, number %in% sample(unique(number), 3)), sentiment_by(review, number)))

Contact

You are welcome to: