Applications in research

Sentiment analysis has received great traction lately (Ravi and Ravi 2015; Pang and Lee 2008), which we explore in the following. Current research in finance and the social sciences utilizes sentiment analysis to understand human decisions in response to textual materials. This immediately reveals manifold implications for practitioners, as well as those involved in the fields of finance research and the social sciences: researchers can use R to extract text components that are relevant for readers and test their hypotheses on this basis. By the same token, practitioners can measure which wording actually matters to their readership and enhance their writing accordingly (Pröllochs, Feuerriegel, and Neumann 2015). We demonstrate below the added benefits in two case studies drawn from finance and the social sciences.

Applications in practice

Several applications demonstrate the uses of sentiment analysis for organizations and enterprises:

• Finance: Investors in financial markets refer to textual information in the form of financial news disclosures before exercising ownership in stocks. Interestingly, they rely not only on quantitative numbers, but also soft information, such as tone and sentiment (Henry 2008; Loughran and McDonald 2011; Tetlock 2007), which thereby strongly influences stock prices. By utilizing sentiment analysis, automated traders can automatically analyze the sentiment conveyed in financial disclosures in order to trigger investment decisions within milliseconds.

• Marketing: Marketing departments are often interested in tracking brand image. For that purpose, they collect large volumes of user opinions from social media and evaluate the feelings of individuals towards brands, products and services. Practitioners in the field of marketing can exploit these insights to enhance their wording according to the feedback of their readership.

• Rating and review platforms: Rating and review platforms fulfill a valuable function by collecting user ratings or preferences for certain products and services. Here, one can automatically process large volumes of user-generated content and exploit the knowledge gained thereby. For example, one can identify which cues convey a positive or negative opinion, or even automatically validate their credibility.

Interface

The SentimentAnalysis package provides interfaces with several other input formats, among which are

• Vector of strings.

• DocumentTermMatrix and TermDocumentMatrix as implemented in the tm package (Feinerer, Hornik, and Meyer 2008).

• Corpus object as implemented by the tm package (Feinerer, Hornik, and Meyer 2008).

We provide examples in the following.

documents <- c("This is good",
               "This is bad",
               "This is inbetween")
convertToDirection(analyzeSentiment(documents)$SentimentQDAP)

## [1] positive negative neutral 
## Levels: negative neutral positive

library(tm)

## Warning: package 'tm' was built under R version 4.3.1

## Loading required package: NLP

corpus <- VCorpus(VectorSource(documents))
convertToDirection(analyzeSentiment(corpus)$SentimentQDAP)

## [1] positive negative neutral 
## Levels: negative neutral positive

dtm <- preprocessCorpus(corpus)
convertToDirection(analyzeSentiment(dtm)$SentimentQDAP)

## [1] positive negative neutral 
## Levels: negative neutral positive

Built-in dictionaries

The SentimentAnalysis package entails three different dictionaries:

• Harvard-IV dictionary

• Henry’s Financial dictionary (Henry 2008)

• Loughran-McDonald Financial dictionary (Loughran and McDonald 2011)

• QDAP dictionary from the package qdapDictionaries

All of them can be manually inspected and even accessed as follows:

# Make dictionary available in the current R environment
data(DictionarHE)

## Warning in data(DictionarHE): data set 'DictionarHE' not found

# Display the internal structure 
str(DictionaryHE)

## List of 2
##  $ negative: chr [1:85] "below" "challenge" "challenged" "challenges" ...
##  $ positive: chr [1:105] "above" "accomplish" "accomplished" "accomplishes" ...

# Access dictionary as an object of type SentimentDictionary
dict.HE <- loadDictionaryHE()
# Print summary statistics of dictionary
summary(dict.HE)

## Dictionary type:  binary (positive / negative)
## Total entries:    97
## Positive entries: 53 (54.64%)
## Negative entries: 44 (45.36%)

data(DictionaryLM)
str(DictionaryLM)

## List of 3
##  $ negative   : chr [1:2355] "abandon" "abandoned" "abandoning" "abandonment" ...
##  $ positive   : chr [1:354] "able" "abundance" "abundant" "acclaimed" ...
##  $ uncertainty: chr [1:297] "abeyance" "abeyances" "almost" "alteration" ...

Dictionary functions

The SentimentAnalysis package distinguishes between three different types of dictionaries. All of them differ by the data they store, which ultimately also controls which methods of sentiment analysis one can apply. The dictionaries are as follows:

• SentimentDictionaryWordlist contains a list of words belonging to a single category. For instance, it can bundle a list of uncertainty words in order to compute the ratio of uncertainty words in that particular document.

• SentimentDictionaryBinary stores two lists of words, one for positive and one for negative entries. This allows one to later compute the polarity of the document on a scale from very positive to very negative. However, the categories are not further distinguished or rated, i.e. all positive words are assigned the same degree of positivity.

• SentimentDictionaryWeighted allows words to take on continuous sentiment scores. This allows one, for instance, to rate increase as being more positive than improve. These weights can then be transformed into a linear model. For this purpose, the SentimentDictionaryWeighted also entails an intercept. It can also store an additional factor in order to revert the weighting by an inverse document frequency.

d <- SentimentDictionaryWordlist(c("uncertain", "possible", "likely"))
summary(d)

## Dictionary type:  word list (single set)
## Total entries:    3

# Alternative call
d <- SentimentDictionary(c("uncertain", "possible", "likely"))
summary(d)

## Dictionary type:  word list (single set)
## Total entries:    3

d <- SentimentDictionaryBinary(c("increase", "rise", "more"),
                               c("fall", "drop"))
summary(d)

## Dictionary type:  binary (positive / negative)
## Total entries:    5
## Positive entries: 3 (60%)
## Negative entries: 2 (40%)

# Alternative call
d <- SentimentDictionary(c("increase", "rise", "more"),
                         c("fall", "drop"))
summary(d)

## Dictionary type:  binary (positive / negative)
## Total entries:    5
## Positive entries: 3 (60%)
## Negative entries: 2 (40%)

d <- SentimentDictionaryWeighted(c("increase", "decrease", "exit"),
                                 c(+1, -1, -10),
                                 rep(NA, 3))
summary(d)

## Dictionary type:  weighted (words with individual scores)
## Total entries:    3
## Positive entries: 1 (33.33%)
## Negative entries: 2 (66.67%)
## Neutral entries:  0 (0%)
## 
## Details
## Average score:      -3.333333
## Median:             -1
## Min:                -10
## Max:                1
## Standard deviation: 5.859465
## Skewness:           -0.6155602

# Alternative call
d <- SentimentDictionary(c("increase", "decrease", "exit"),
                         c(+1, -1, -10),
                         rep(NA, 3))
summary(d)                         

## Dictionary type:  weighted (words with individual scores)
## Total entries:    3
## Positive entries: 1 (33.33%)
## Negative entries: 2 (66.67%)
## Neutral entries:  0 (0%)
## 
## Details
## Average score:      -3.333333
## Median:             -1
## Min:                -10
## Max:                1
## Standard deviation: 5.859465
## Skewness:           -0.6155602

Performance evaluation

Ultimately, several routines allow one to exlore the generated dictionary further. On the one hand, a simple overview can be displayed by means of the summary() routine. On the other hand, a Kernel Density Estimation can also visualize the distribution of positive and negative words. For instance, one can identify whether the opinionated words were skewed to either end of the polarity scale. Lastly, the compareDictionary() routine can compare the generated dictionary to dictionaries from the literature. It automatically computes various metrics, among which are the overlap or the correlation.

compareDictionaries(dict,
                    loadDictionaryQDAP())

## Comparing: wordlist vs weighted
## 
## Total unique words: 4213
## Matching entries: 2 (0.0004747211%)
## Entries with same classification: 0 (0%)
## Entries with different classification: 2 (0.0004747211%)
## Correlation between scores of matching entries: 1

## $totalUniqueWords
## [1] 4213
## 
## $totalSameWords
## [1] 2
## 
## $ratioSameWords
## [1] 0.0004747211
## 
## $numWordsEqualClass
## [1] 0
## 
## $numWordsDifferentClass
## [1] 2
## 
## $ratioWordsEqualClass
## [1] 0
## 
## $ratioWordsDifferentClass
## [1] 0.0004747211
## 
## $correlation
## [1] 1

sentiment <- predict(dict, documents)
compareToResponse(sentiment, response)

##                             Dictionary
## cor                         0.94868330
## cor.t.statistic             5.19615237
## cor.p.value                 0.01384683
## lm.t.value                  5.19615237
## r.squared                   0.90000000
## RMSE                        0.23301039
## MAE                         0.20001111
## Accuracy                    1.00000000
## Precision                   1.00000000
## Sensitivity                 1.00000000
## Specificity                 1.00000000
## F1                          1.00000000
## BalancedAccuracy            1.00000000
## avg.sentiment.pos.response  0.45116801
## avg.sentiment.neg.response -0.67675202

plotSentimentResponse(sentiment, response)

## `geom_smooth()` using formula = 'y ~ s(x, bs = "cs")'

## Warning: Computation failed in `stat_smooth()`
## Caused by error in `smooth.construct.cr.smooth.spec()`:
## ! x has insufficient unique values to support 10 knots: reduce k.

The following example demonstrates how a calculated dictionary can be used for predicting the sentiment of out-of-sample data. In addition, the code then evaluates the prediction performance by comparing it to the built-in dictionaries.

test_documents <- c("This is neither good nor bad",
                    "What a good idea!",
                    "Not bad")
test_response <- c(0, 1, 1)

pred <- predict(dict, test_documents)

compareToResponse(pred, test_response)

##                              Dictionary
## cor                        5.922189e-05
## cor.t.statistic            5.922189e-05
## cor.p.value                9.999623e-01
## lm.t.value                 5.922189e-05
## r.squared                  3.507232e-09
## RMSE                       8.523018e-01
## MAE                        6.666521e-01
## Accuracy                   3.333333e-01
## Precision                  0.000000e+00
## Sensitivity                         NaN
## Specificity                3.333333e-01
## F1                                  NaN
## BalancedAccuracy                    NaN
## avg.sentiment.pos.response 1.457684e-05
## avg.sentiment.neg.response          NaN

plotSentimentResponse(pred, test_response)

## `geom_smooth()` using formula = 'y ~ s(x, bs = "cs")'

## Warning: Computation failed in `stat_smooth()`
## Caused by error in `smooth.construct.cr.smooth.spec()`:
## ! x has insufficient unique values to support 10 knots: reduce k.

compareToResponse(analyzeSentiment(test_documents), test_response)

## Warning in cor(sentiment, response): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(x, y): the standard deviation is zero

## Warning in cor(sentiment, response): the standard deviation is zero

##                             WordCount SentimentGI NegativityGI PositivityGI
## cor                        -0.8660254 -0.18898224   0.18898224  -0.18898224
## cor.t.statistic            -1.7320508 -0.19245009   0.19245009  -0.19245009
## cor.p.value                 0.3333333  0.87896228   0.87896228   0.87896228
## lm.t.value                 -1.7320508 -0.19245009   0.19245009  -0.19245009
## r.squared                   0.7500000  0.03571429   0.03571429   0.03571429
## RMSE                        1.8257419  1.19023807   0.60858062   0.67357531
## MAE                         1.3333333  0.83333333   0.44444444   0.61111111
## Accuracy                    1.0000000  0.66666667   1.00000000   1.00000000
## Precision                         NaN  0.00000000          NaN          NaN
## Sensitivity                       NaN         NaN          NaN          NaN
## Specificity                 1.0000000  0.66666667   1.00000000   1.00000000
## F1                                NaN         NaN          NaN          NaN
## BalancedAccuracy                  NaN         NaN          NaN          NaN
## avg.sentiment.pos.response  2.0000000 -0.16666667   0.44444444   0.27777778
## avg.sentiment.neg.response        NaN         NaN          NaN          NaN
##                            SentimentHE NegativityHE PositivityHE SentimentLM
## cor                        -0.18898224           NA  -0.18898224 -0.18898224
## cor.t.statistic            -0.19245009           NA  -0.19245009 -0.19245009
## cor.p.value                 0.87896228           NA   0.87896228  0.87896228
## lm.t.value                 -0.19245009           NA  -0.19245009 -0.19245009
## r.squared                   0.03571429           NA   0.03571429  0.03571429
## RMSE                        0.67357531    0.8164966   0.67357531  1.19023807
## MAE                         0.61111111    0.6666667   0.61111111  0.83333333
## Accuracy                    1.00000000    1.0000000   1.00000000  0.66666667
## Precision                          NaN          NaN          NaN  0.00000000
## Sensitivity                        NaN          NaN          NaN         NaN
## Specificity                 1.00000000    1.0000000   1.00000000  0.66666667
## F1                                 NaN          NaN          NaN         NaN
## BalancedAccuracy                   NaN          NaN          NaN         NaN
## avg.sentiment.pos.response  0.27777778    0.0000000   0.27777778 -0.16666667
## avg.sentiment.neg.response         NaN          NaN          NaN         NaN
##                            NegativityLM PositivityLM RatioUncertaintyLM
## cor                          0.18898224  -0.18898224                 NA
## cor.t.statistic              0.19245009  -0.19245009                 NA
## cor.p.value                  0.87896228   0.87896228                 NA
## lm.t.value                   0.19245009  -0.19245009                 NA
## r.squared                    0.03571429   0.03571429                 NA
## RMSE                         0.60858062   0.67357531          0.8164966
## MAE                          0.44444444   0.61111111          0.6666667
## Accuracy                     1.00000000   1.00000000          1.0000000
## Precision                           NaN          NaN                NaN
## Sensitivity                         NaN          NaN                NaN
## Specificity                  1.00000000   1.00000000          1.0000000
## F1                                  NaN          NaN                NaN
## BalancedAccuracy                    NaN          NaN                NaN
## avg.sentiment.pos.response   0.44444444   0.27777778          0.0000000
## avg.sentiment.neg.response          NaN          NaN                NaN
##                            SentimentQDAP NegativityQDAP PositivityQDAP
## cor                          -0.18898224     0.18898224    -0.18898224
## cor.t.statistic              -0.19245009     0.19245009    -0.19245009
## cor.p.value                   0.87896228     0.87896228     0.87896228
## lm.t.value                   -0.19245009     0.19245009    -0.19245009
## r.squared                     0.03571429     0.03571429     0.03571429
## RMSE                          1.19023807     0.60858062     0.67357531
## MAE                           0.83333333     0.44444444     0.61111111
## Accuracy                      0.66666667     1.00000000     1.00000000
## Precision                     0.00000000            NaN            NaN
## Sensitivity                          NaN            NaN            NaN
## Specificity                   0.66666667     1.00000000     1.00000000
## F1                                   NaN            NaN            NaN
## BalancedAccuracy                     NaN            NaN            NaN
## avg.sentiment.pos.response   -0.16666667     0.44444444     0.27777778
## avg.sentiment.neg.response           NaN            NaN            NaN

Configuration of preprocessing

When desired, one can implement a tailored preprocessing stage that adapts to specific needs. The following code snippets demonstrate such adaptation. In particular, the SentimentAnalysis package ships a function ngram_tokenize() in order to extract -grams from the corpus. This does not affect the results of the built-in dictionaries but rather changes the features used as part of dictionary generation.

corpus <- VCorpus(VectorSource(documents))
tdm <- TermDocumentMatrix(corpus, 
                          control=list(wordLengths=c(1,Inf), 
                                       tokenize=function(x) ngram_tokenize(x, char=FALSE, 
                                                                           ngmin=1, ngmax=2)))
rownames(tdm)

##  [1] "a"           "a bad"       "a good"      "a very"      "bad"        
##  [6] "bad thing."  "good"        "good thing!" "is"          "is a"       
## [11] "is okay."    "okay."       "thing!"      "thing."      "this"       
## [16] "this is"     "very"        "very bad"    "very good"

dict <- generateDictionary(tdm, response)
summary(dict)

## Dictionary type:  weighted (words with individual scores)
## Total entries:    7
## Positive entries: 3 (42.86%)
## Negative entries: 4 (57.14%)
## Neutral entries:  0 (0%)
## 
## Details
## Average score:      2.136424e-06
## Median:             -5.891262e-05
## Min:                -0.4368759
## Max:                0.4380331
## Standard deviation: 0.3016221
## Skewness:           0.004124293

dict

## Type: weighted (words with individual scores)
## Intercept: -3.230431e-05
## -0.44 bad
## -0.29 very bad
## -0.00 bad thing.
## -0.00 thing.
##  0.00 good thing!
##  0.29 a good
##  0.44 good

Performance optimization

Once the user has decided upon a preferred rule, he can adapt the analyzeSentiment() routine by restricting it to calculate only the rules of interest. Such behavior can be implemented by changing the default value of the argument rules. See the following code snippets for an example:

sentiment <- analyzeSentiment(documents,
                              rules=list("SentimentLM"=list(ruleSentiment, loadDictionaryLM())))
sentiment

##   SentimentLM
## 1         0.5
## 2         0.5
## 3         0.0
## 4        -0.5
## 5        -0.5

Language support and extensibility

SentimentAnalysis can be adapted for use with languages other than English. In order to do this, one needs to introduce changes at two points:

• Preprocessing: The built-in routines use a parameter language="english" to perform all preprocessing operations for the English language. Instead, one might prefer to change stemming and stopwords to a desired language. If one wishes to make further changes to the preprocessing, it might be beneficial to replace the automatic preprocessing with one’s own routines, which then return a DocumentTermMatrix.

• Dictionary: If one has a response or baseline variable, one can use the dictionary generation approach that is shipped with SentimentAnalysis. This can then automatically generate a dictionary of positive and negative words that can be applied to the given language. Otherwise, if one has no baseline variable at hand, one needs to load a dictionary for that langauge. It might be worthwhile to search online for pre-defined lists of positive and negative words.

The following example demonstrates how SentimentAnalysis can be adapted to work with a sample in German. Here, we supply a positive and negative document in the variable documents. Afterwards, we introduce a very small dictionary of positive and negative words, which is stored in dictionaryGerman. Finally, we use analyzeSentiment() to perform a sentiment analysis, where we introduce changes as follows: first of all, we supply language="german" to ensure that all preprocessing operations are being made for the German language. Additionally, we define our custom rule for GermanSentiment that uses our previous, customized dictionary.

documents <- c("Das ist ein gutes Resultat",
               "Das Ergebnis war schlecht")
dictionaryGerman <- SentimentDictionaryBinary(c("gut"), 
                                              c("schlecht"))

sentiment <- analyzeSentiment(documents,
                              language="german",
                              rules=list("GermanSentiment"=list(ruleSentiment, dictionaryGerman)))
sentiment

##   GermanSentiment
## 1             0.0
## 2            -0.5

convertToBinaryResponse(sentiment$GermanSentiment)

## [1] positive negative
## Levels: negative positive

Similarly, one can implement a dictionary with custom sentiment scores.

woorden <- c("goed","slecht")
scores <- c(0.8,-0.5)
dictionaryDutch <- SentimentDictionaryWeighted(woorden, scores)
documents <- "dit is heel slecht"
sentiment <- analyzeSentiment(documents,
                              language="dutch",
                              rules=list("DutchSentiment"=list(ruleLinearModel, dictionaryDutch)))
sentiment

##   DutchSentiment
## 1           -0.5

Notes:

• The argument rules is a named list of approaches, where each entry specifies a combination of a rule and a dictionary.

• Caution is needed when working with stemming. The default routines of SentimentAnalysis automatically perform stemming. Therefore, it is necessary to included stemmed terms in the original dictionary. One can easily achieve such a conversion by calling tm::stemDocument().