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
Functions
There are two main functions (top 2 in table below) in sentimentr with several helper functions summarized in the table below:
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")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 0.4489512 0.10245522
## 2: OBAMA time 2 7476 0.3878883 0.08730120
## 3: OBAMA time 3 7241 0.4408708 0.08788677
## 4: ROMNEY time 1 4085 0.3669465 0.05259171
## 5: ROMNEY time 2 7534 0.3271200 0.04277505
## 6: ROMNEY time 3 8302 0.3866709 0.07109706
## 7: CROWLEY time 2 1672 0.2279950 0.06725135
## 8: LEHRER time 1 765 0.3634981 0.13377765
## 9: QUESTION time 2 583 0.3282897 0.02209842
## 10: SCHIEFFER time 3 1445 0.3810998 0.06892295Plotting
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 ?polarity_table & ?valence_shifters_table
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] FALSEIt 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] TRUEThe 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 -0.7594893You can see the values of a key that correspond to a word using data.table syntax:
mykey[c("a", "b")][[2]]
## [1] -0.2537805 -0.1951504Updating (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 -0.632599Next 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 -0.7071068Annie 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.
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/sentimentr", "trinker/stansent")
pacman::p_load(syuzhet, qdap, microbenchmark, RSentiment)
package 'microbenchmark' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\Tyler\AppData\Local\Temp\RtmpwJpARf\downloaded_packages
package 'RSentiment' successfully unpacked and MD5 sums checked
The downloaded binary packages are in
C:\Users\Tyler\AppData\Local\Temp\RtmpwJpARf\downloaded_packages
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, 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 0 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, 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() 27555.9649 29336.0280 30124.1588 31116.0911
sentimentr_hu_liu() 262.6678 267.3538 272.7080 272.0398
sentimentr_sentiword() 1057.2413 1087.2625 1102.4927 1117.2838
RSentiment() 145682.8551 148026.6238 151587.5462 150370.3924
syuzhet_binn() 394.5227 439.8974 462.2086 485.2720
syuzhet_nrc() 980.6292 1003.1331 1024.1288 1025.6370
syuzhet_afinn() 169.6761 173.3827 181.3841 177.0894
uq max neval
31408.2557 31700.4203 3
277.7281 283.4165 3
1125.1184 1132.9531 3
154539.8918 158709.3912 3
496.0516 506.8311 3
1045.8786 1066.1202 3
187.2381 197.3868 3Comparing 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.
- Kotzias, D., Denil, M., De Freitas, N., & Smyth,P. (2015). From group to individual labels using deep features. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 597-606. http://mdenil.com/media/papers/2015-deep-multi-instance-learning.pdf
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.
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:
- Larger dictionaries discriminate better (Sentiword [n = 20,100] vs. Hu & Lu [n = 6,872])
- 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:
- 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