sentimentr
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
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 c**w (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 p**w 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 (p**w) and defaults to 4 words
before and two words after p**w to be considered as valence shifters.
The cluster can be represented as
(ci, j, l = {p**wi, j, k − n**b, ..., p**wi, j, k, ..., p**wi, j, k − n**a}),
where n**b & n**a 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 (p**w).
Pause (c**w) 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{p**wi, j, k − n**b, 1, max{c**wi, j, k < p**wi, j, k}}
and the upper bound is constrained to
min{p**wi, j, k + n**a, wi, j**n, min{c**wi, j, k > p**wi, j, k}}
where wi, j**n 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 (wbut conjunction, ..., wi, j, kp) up-weights the cluster by 1 + z2 * {|wbut conjunction|, ..., wi, j, kp} (.85 is the default weight (z2) where |wbut conjunction| 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, ..., |wbut conjunction| * − 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 (c′i, j) and divided by the square root of the word count (√wi, j**n) yielding an unbounded polarity score (δi, j) for each sentence.
δi**j = c'i**j/√wijn
Where:
c′i, 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′ = ∑(|wbut conjunction|, ..., wi, j, kp, wi, j, kp, ..., |wbut conjunction| * − 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_gh("trinker/sentimentr")Usage
There are two main functions in sentimentr with three helper functions summarized in the table below:
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 0.0000000To 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 0.4024922To 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 0.4397613 0.10966120
## 2: LEHRER time 1 765 0.3493838 0.10941383
## 3: OBAMA time 3 7241 0.4135144 0.09654523
## 4: OBAMA time 2 7476 0.3832811 0.08893467
## 5: ROMNEY time 3 8302 0.3909338 0.08108205
## 6: ROMNEY time 1 4085 0.3510066 0.06613552
## 7: SCHIEFFER time 3 1445 0.3772378 0.06515716
## 8: CROWLEY time 2 1672 0.2125288 0.05531121
## 9: ROMNEY time 2 7534 0.3188779 0.04946325
## 10: QUESTION time 2 583 0.3255268 0.03334828Plotting
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))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), and my
own algorithm with both the default Hu & Liu
(2004) polarity
lexicon as well as Baccianella, Esuli and Sebastiani's
(2010) SentiWord lexicon.
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/sentimentr", "trinker/stansent")
pacman::p_load(syuzhet, qdap, microbenchmark)
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),
hu_liu = round(sentiment(ase, question.weight = 0)[["sentiment"]], 2),
sentiword = round(sentiment(ase, sentiword, question.weight = 0)[["sentiment"]], 2),
syuzhet,
sentences = ase,
stringsAsFactors = FALSE
), "sentences")
stanford hu_liu sentiword bing afinn nrc
1 0.5 0 0.27 -1 -2 0
2 -1 0.8 0.65 1 3 1
3 -0.5 0.5 0.32 1 3 1
4 0.5 0 0 1 3 1
5 0.5 -0.41 -0.56 1 3 1
6 0.5 0.06 0.05 1 3 1
7 0.5 -0.38 -0.05 1 2 1
8 0 0 -0.14 0 0 -1
9 0.5 0.38 0.24 -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.
ase_100 <- rep(ase, 100)
stanford <- function() {sentiment_stanford(ase_100)}
sentimentr_hu_liu <- function() sentiment(ase_100)
sentimentr_sentiword <- function() sentiment(ase_100, sentiword)
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(),
syuzhet_binn(),
syuzhet_nrc(),
syuzhet_afinn(),
times = 3
)
Unit: milliseconds
expr min lq mean median
stanford() 19534.8874 19719.3494 19782.8247 19903.8114
sentimentr_hu_liu() 220.7847 224.1138 226.0406 227.4429
sentimentr_sentiword() 969.6914 973.4066 979.2458 977.1219
syuzhet_binn() 356.9010 357.5310 363.1912 358.1610
syuzhet_nrc() 884.7328 892.4310 914.2375 900.1292
syuzhet_afinn() 162.0473 162.6307 172.1710 163.2141
uq max neval
19906.7934 19909.7754 3
228.6686 229.8943 3
984.0230 990.9240 3
366.3362 374.5115 3
928.9898 957.8504 3
177.2328 191.2516 3Contact
You are welcome to:
- submit suggestions and bug-reports at: https://github.com/trinker/sentimentr/issues
- send a pull request on: https://github.com/trinker/sentimentr/
- compose a friendly e-mail to: tyler.rinker@gmail.com