=Paper=
{{Paper
|id=Vol-2454/paper_14
|storemode=property
|title=GerVADER - A German Adaptation of the VADER Sentiment Analysis Tool for Social Media Texts
|pdfUrl=https://ceur-ws.org/Vol-2454/paper_14.pdf
|volume=Vol-2454
|authors=Karsten Tymann,Matthias Lutz,Patrick Palsbröker,Carsten Gips
|dblpUrl=https://dblp.org/rec/conf/lwa/TymannLPG19
}}
==GerVADER - A German Adaptation of the VADER Sentiment Analysis Tool for Social Media Texts==
https://ceur-ws.org/Vol-2454/paper_14.pdf
GerVADER - A German adaptation of the
VADER sentiment analysis tool for social media
texts
Karsten Michael Tymann, Matthias Lutz, Patrick Palsbröker, and Carsten Gips
FH Bielefeld University of Applied Sciences, Minden, Germany
ktymann@fh-bielefeld.de, matthias.lutz@fh-bielefeld.de,
patrick.palsbroeker@fh-bielefeld.de, carsten.gips@fh-bielefeld.de
https://www.fh-bielefeld.de
Abstract. For the English language sentiment analysis tools are fairly
popular. One is called VADER [1] which offers a rather simple process for
sentiment classification. Due to its lexicon-based approach with a design
focus on social media texts, no additional training data is required. In this
paper the process of creating VADER is applied to build a German adap-
tation which is called GerVADER. The paper will present the concept of
VADER and how a German version can be built within reasonable time.
GerVADER uses SentiWS as a starting point for the lexicon, combines
it with language independent parts of the VADER lexicon and copies
the process of having users rate the words intensity and polarity. The
next step is comprised of comparing the algorithmically changes due to
the natural differences in language between German and English. Then
GerVADER is compared to the results of the SB10k [2] corpus classi-
fication which contains more than 9000 human labeled tweets. Finally
GerVADER is tested with parts of the SCARE [3] dataset which contains
reviews for mobile apps. The results show that GerVADER lacks some
additional work to increase its classification accuracy, but it promises
better results considering how well the original performed.
Keywords: VADER· German sentiment analysis· SB10k · SCARE
1 Introduction
Sentiment analysis is often based on machine learning which requires lots of
data and sometimes even additional human work, e.g. for labeling the data
beforehand. In the German language collecting reasonable amounts of data for
machine learning is quite difficult, since not many work has been done in the
field yet. This was the motivation of this work to build an own corpus and label a
reasonable amount of it for training purposes. Especially for the domain of social
media and micro blogging the internet is lacking up-to-date German corpora to
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2 K. M. Tymann et al.
bootstrap one’s sentiment analysis tool. While there exist corpora like SB10k or
DAI [6], those are not available to the public and can be difficult to obtain in
its entirety, given how companies like Twitter handle their data policy. Even if
one obtains the corpora it still requires lots of research and additional work to
get a running sentiment analysis tool for the German language.
Another crucial factor for a sentiment analysis tool can be lexicons. For the
German language there are multiple lexicons free to use. SentiWS [4] for example
is a German lexicon with polarity and intensity. For every word there are multiple
grammatical forms, e.g. the plural form of the word.
GermanPolarityClues (GPC) [5] is another resource for a German dictionary,
however the project seems abandoned and superseded by SentiWS. Nevertheless,
it is stated that in tests GPC reached a F1 score of up to 0.88.
Both of the mentioned lexicons are not adapted to the social media domain
and lack multiple linguistic features that are common in such a domain.
This paper will show how GerVADER builds upon the good results of VADER
with its own lexicon. VADER reached great classification accuracy for microblog-
ging platforms (up to F1 = 0.96) and was able to score better results than human
raters in some cases. VADER is free to use, requires no knowledge of machine
learning and can be easily executed and expanded with Python or its multiple
adaptations in other programming languages.
It will be shown how GerVADERs lexicon was built with the SentiWS lexi-
con and parts of VADERs lexicon. In the next step the lexicon has been rated
by a crowd and then cleaned of ambiguous data. Afterwards the grammatical
and lexical heuristics used in VADER (e.g. negation words) have been manually
adjusted to the German language. Then GerVADER has been tested on parts
of the SB10k corpus as well as on a subset of app reviews of the SCARE corpus.
GerVADER scores mediocre ratings (F1=0.36-0.70) depending on the test cor-
pus and allows future tools to compete against it in the mentioned datasets. The
scores hint that GerVADER has still unexploited potential and that the Ger-
man language might need additional grammatical rules for an improved VADER
adaptation. Especially the correct classificaton of negative sentiments is lacking
as well as further testing of GerVADER with different corpora and its usefulness
in other domains.
GerVADER was developed as part of a student project and will be free to
download and to use like VADER [12].
2 Background
In this section the original VADER tool for the English language will be de-
scribed. Furthermore, the SentiWS lexicon, which was used as a basis for Ger-
VADERs lexicon, as well as the corpora SB10k and SCARE, which were used
for evaluating GerVADER, will be introduced.
GerVADER 3
2.1 VADER
VADER is short for ”Valence Aware Dictionary and sEntiment Reasoner” and
is available under the MIT License. The tool was published in 2014 and is espe-
cially focused on social media texts. It uses a lexicon driven approach as well as
additional heuristics for rating the input. Since VADER is not a machine learn-
ing approach it offers consistent ratings and requires no training data. VADER
achieved some remarkable scores for multiple domains such as tweets, movie or
product reviews. The development of VADER can be split into seven steps:
Gather lexical features of established sentiment lexicons: The cre-
ators of VADER first researched for existing sentiment lexicons like LIWC,
ANEW and GI. They took parts (words) of it and integrated them into their
own VADER lexicon.
Gather lexical features characteristic for microblogging domains:
Texts on social media and other microblogging platforms have their own unique
characteristics. The creators gathered emoticons, domain specific words and
other abbreviations from these platforms and integrated them into the lexicon.
Rate lexical feature candidates: In this step the creators gathered a
crowd in order to rate the words individually in their intensity and polarity. Ev-
ery rater received batches of 25 words, in which five words have been intentionally
integrated that function as a gold-standard validation. If a user rates three or
more of the five gold-standard validation words wrong, the whole batch will be
discarded. The gold-standard words have been manually set and do not seem to
be available for download. In [1] it is stated that for good results the partici-
pants have received financial compensation. Also, the raters have been carefully
selected. Every rater had to pass a reading comprehension test and took part
in an online sentiment training. At the end of the rating process VADER had
more than 9000 words rated with 10 individual ratings each in the range of very
negative (-4) to neutral (0) to positive (+4).
Filtering: In this step the lexicon has been cleaned from inconclusive words.
These words were either rated neutral in total by the crowd, or the crowd was
divided over the polarity and intensity of the word. This means that the standard
deviation of the word was 2.5 or higher and thus resulting in a value that cannot
be trusted for a sentiment classification. After the filtering the lexicon contained
more than 7.500 words.
Building human heuristics: VADER contains five heuristics that can shift
or boost the sentiment of a sentence. These heuristics include punctuation marks,
capitalization (words in all caps), booster words (negative and positive, e.g.
words like ”amazingly”), contrastive conjunctions and words that negate a sen-
tence (e.g. ”not”, ”won’t”). When a sentence is being rated these keywords are
identified and can shift or impact the rating.
Evaluate heuristics: In order to evaluate how much the gathered heuristics
can influence a sentiment, the authors have conducted a controlled experiment
with 30 tweets that have been manually modified into different versions which
include the features explained in subsection 2.1. Those tweets have been mixed
into other tweets and were again rated by a crowd. As a result, the authors
4 K. M. Tymann et al.
were able to analyze how much a lexical or grammatical feature can impact the
sentiment of a tweet. The findings were integrated into VADERs heuristics.
Evaluation and results: In the last step VADER was tested in four differ-
ent domains against other established lexicon-based approaches. The domains
included social media text, movie and product reviews as well as newspaper ar-
ticles. The results have shown that VADER outperforms every other lexicon in
every domain and is even able to outperform human raters in the domain of
social media texts. Against machine learning models (NB, ME, SVM) VADER
was ranked first in three of the four domains. Only in the movie domain the
Naive Bayes and Maximum Entropy methods (both trained on movie corpora)
were able to reach better results than VADER (F1 = 0.75 vs. F1 = 0.61). In
summary VADER achieves good scores in the social media domain. Another
benefit of the approach is that VADER does not require any training and rates
consistently. On the downside VADER does not detect irony as well as longer
more complex sentences might be rated wrongly, since the heuristics only apply
to small ranges of words. Additionally, VADER does not detect phrases but will
rate every single word individually which can lead to wrong conclusions.
2.2 SentiWS
SentiWS is a German lexicon for sentiment analysis. It offers 1.644 positive and
1.827 negative words with a polarity and intensity range from -1 to 1. All words
are given in their base form and additionally in variations like plural form for
nouns or tenses for verbs (see Table 1). Therefore, while only the basic form is
rated one can easily transfer the rating to its grammatical variations. SentiWS
has been lastly updated at the end of 2018 and is therefore an up-to-date resource
for a German sentiment lexicon.
Table 1. SentiWS extract: Positive words
Word POS Polarity Forms
Anspruch NN 0.0040 Anspruchs,Anspruches, ...
agil ADJX 0.1959 agilstes,agilster, ...
ehren VVINF 0.0612 ehret,ehrest, ...
2.3 SB10k
SB10k is a German Twitter corpus with almost 10.000 tweets. It consists of
human labeled tweets that can be used for machine learning algorithms for sen-
timent analysis. In the paper the authors compared how two different classifiers
(SVM and CNN) performed on the SB10k corpus as well as on two other addi-
tional German corpora (DAI and MGS [7]). Their results showed that the best
rating for the SB10k corpus was a F1 = 0.65 classification. The corpus is freely
available for download (as collection of the relevant Twitter IDs) [2]. SB10ks
corpus and classification results will serve as benchmark in section 4.
GerVADER 5
2.4 SCARE
SCARE is another corpus that offers around 800.000 app reviews for different
app categories from the Google Play Store. The reviews have an id, a star rating
from 1 to 5, a review headline and a review text. Some categories and reviews
will be used for evaluating GerVADERs performance in the app review domain.
3 Process
While VADER started from scratch, for GerVADER some steps can be copied
while other steps have to be replicated accordingly for the German language.
Just like VADER the development of GerVADER starts with the creation of the
lexicon, followed by the ratings of the crowd, filtering of ambiguous words, an
adaptation of the booster and negation words including some smaller changes
on the source code and lastly the classification tests.
3.1 Constructing the initial lexicon
The initial lexicon is based on the SentiWS dataset. Only the basic forms of the
words have been taken into consideration thus resulting in a sum of 3471 words
(1.644 positive, 1.827 negative). Additionally, unique German terms have been
added to the lexicon that are commonly used in slang expressions and on social
media platforms (see Fig. 1). Note however that only single words have been
taken account of since phrases cannot be part of the lexicon. Two sources were
taken into consideration for the additional words.
One of them is from Langenscheidt which is a German company for lan-
guage and language-to-language dictionaries. Langenscheidt [8] is in Germany
also known for their annual ranking of teenager slang words (”Jugendwörter des
Jahres” [9]). Every year Langenscheidt collects words that are commonly and
exclusively used by teenagers and young adults. This contest results in the pub-
lication of the Top10 ”Jugendwörter des Jahres”. The Top3 words of the years
2008-2017 have been added to the lexicon as well as the Top10 words of the year
2018 [10]. Only single words have been taken into consideration.
The second source is a website that collects German slang words [11]. Several
single words have been manually selected and were added to the lexicon.
Both sources added together have contributed 80 words to the initial lexicon
resulting in an overall size of the initial lexicon of 3.546 words. All words are
contained in the same lexicon without any initial polarity rating. In the next step
a crowd contributed with their individual ratings to the lexicon (see Fig. 1).
For the validation lexicon that is used for validating a user rated batch, a
manually written lexicon was created. It consists of more than 100 words. The
most positive and most negative words in VADER (e.g. words like ”death” or
”hell”) were collected and manually translated by the author. The gold standard
words did not block off any lexicon words so that a word like ”Tod” (i.e. ”death”)
was still able to be rated by the user.
6 K. M. Tymann et al.
Fig. 1. GerVADER lexicon creation
3.2 Crowd-rating of the lexicon
For GerVADER the crowd consisted of fellow students and friends. The crowd
was introduced to the project but did not have to pass any tests or trainings.
The list of participants has not been shared so that no rater knew who the other
raters were in order to block any communication within the crowd.
For the rating platform a custom-made application has been developed. The
raters have been given access to the website with everyone receiving a username
and a password. On the site were two main sections.
The first section was a tutorial section where the functionality of the site was
briefly explained.
The second section was the component of rating the words in which the user
was introduced to a randomly generated batch consisting of 25 words. The server
kept track of every raters progress and returned within the batch 20 words that
the rater had not yet rated plus 5 gold standard words. The user was then able
to rate the words according to the -4 to +4 scale. After rating a batch, the user
was allowed to submit it. The server then checked whether the ratings were valid
and reviewed whether the gold standard words have been rated correctly. If three
or more words of the gold standard words were rated significantly different, the
batch has been dropped without notifying the user. The ratings were then not
saved so that the user was still able to rate the words in another batch.
Since no participant was financially compensated, motivation was a huge
factor. In order to tackle this problem each rater was linked with an animal
image. On the main page the number of already rated words was shown as
well as the animal pictures of raters who have rated one or more batches on
the according day. Thus, feelings of competition and cooperation were invoked.
This update was done one week after the release and increased the participation
rate of the crowd significantly. Furthermore, a graphic showing the number of
rated words by each rater was created periodically and sent to every user via
email. Both steps were necessary since the participation was overall not enough
to create a lexicon with every word having 10 individual ratings. Within one
month all words received round about 7 individual ratings.
GerVADER 7
3.3 Generating the final lexicon
Similar to VADER, words that have a neutral rating were filtered out for the
final lexicon. Also, words with a standard deviation of 2.5 or higher have been
removed. For the finalization of the lexicon there have been two additional steps.
The first step involved the VADER lexicon. Since most emoticons and English
abbreviations are also common in German social media texts, more than 800
words of this type have been added to GerVADER with their original intensity.
Therefore, the users had not to rate common terms like ”lol” or ”:)” (see Fig. 1).
The second step was to take into consideration that VADER does not do
any kind of pre-processing. So the words from the lexicon are directly compared
with the words of the sentence being analyzed. However, SentiWS offers for every
word multiple grammatical forms. Therefore, those grammatical forms have been
added to the lexicon, meaning that every single one of them represents its own
word in the lexicon. They received the same rating as the basic form. With the
expansion of the lexicon by grammatical forms the size of the lexicon increased
to more than 34.000 words.
3.4 Adapting the VADER heuristics
The heuristics are part of the source code and are in some parts not exclusive to
the English language. Characteristics like capitalization of words or punctuation
marks convey the same meaning for both the German and English languages.
Only 3 of the 5 heuristics had to be adapted for the German language. These
heuristics revolve around booster, negation and contrast conjunction words. For
GerVADER the English words have been simply translated to German.
3.5 Evaluation and additional steps
Before evaluation, another adjustment has been done to the algorithm. While
comparing the words from the text to the lexicon words, the inspected word is
transformed to all lower cases. Since the lexicon however contains words with
upper and lower cases that are identical and just differ from their POS tag
(e.g. ”Anstieg” noun and ”anstieg” verb), the lowercase transformation had to
be edited. In the lexicon, words can have a different intensity whether it is for
example a noun (written in capital in German) or a verb. However, users usually
do not care about the correct capitalization of the words. Thus, the following
adjustment has been made:
1. Check if the currently inspected word can be found in the lexicon
2. If not, transform the word to all lower cases and recheck the lexicon
3. If not, only capitalize the first letter of the word and recheck the lexicon
At the end of this process if the word has not been found in the lexicon
in any of these steps, the next word will be inspected. This adjustment allows
to match more words without relying on the user to write the word correctly
8 K. M. Tymann et al.
regarding the capitalization. Apart from that adjustment no further adjustments
have been done on the VADER algorithm.
For the performance evaluation of GerVADER, the SB10k and parts of the
SCARE corpus have been used. Since tweets can be deleted, only 7.000+ tweets
(about 70%) of the original SB10k corpus could be collected. Therefore, com-
paring the results to the original results of the authors is not fully reliable.
Additionally, in [2] it is left open on which 10% of the corpus the authors have
tested on. To conquer this problem, GerVADER comes with the corpus as well
as another dataset containing only 10% of the SB10k corpus. For future work
this will allow for comparison.
For the SCARE corpus a selection of review categories has been made. Since
the classification labels of user reviews are given as stars (1-5), before being able
to classify the data with GerVADER the star ratings first have to be translated
to as either positive, negative or neutral. To do this as simply as possible, 1+2-
star ratings are interpreted as negative, 3 as neutral and 4+5 as positive. The
headline will be merged into the comment. If the headline has no punctuation
mark at the end of the headline, a dot will be put in between the headline
and review text. The idea is to prevent that words from the headline influence
the sentiment of the review comment. However, in some cases this might be a
problem if the user started a sentence in the headline and continued it in the
text, but we assume that these cases are neglectable rare. Only two app review
categories will be tested.
4 Results
For testing the performance of GerVADER the obtained SB10k corpus, 10%
of the SB10k corpus as well as parts of the SCARE corpus were taken into
consideration (see Table 2). For every test the precision, recall and F1 score
for every label (pos, neg, neu) is measured. The total F1 score that will be the
deciding factor in how well GerVADER and the other classifiers perform is only
calculated with the positive and negative F1 score. Additionally, the sum of
F 1pos , F 1neg and F 1neu will be calculated and called F 13 .
Starting with the SB10k corpus, the corpus is already human-labeled and
requires no additional work. The results for 7476 tweets (pos, neg, neu) show an
overall F1 score of 39,42% (see Table 2, No. 1). The positive F1 score is 43,54%
while the negative F1 score is 35,30%. Overall the results show a good recall for
positive tweets and a good precision for neutral tweets, however the numbers
for the other criteria and labels are less than 50% (see Fig. 2, No. 1). These
low numbers decrease the F1 scores significantly. The numbers show that while
most positive tweets are classified correctly, the numbers for the negative and
neutral tweets are less accurate. Especially for the neutral tweets many tweets
have been wrongly classified as positive. The negative tweets however have been
almost evenly distributed on all three labels which hints at a problem with
detecting negation words. GerVADER achieves better results with filtering out
the neutral statements beforehand. In these test cases the F1 score is circa 64%
GerVADER 9
Table 2. GerVADER results - NN = no neutrals (filtered beforehand), N = neutral,
P = positive, F 13 = (F 1pos + F 1neg + F 1neu )/3
No. Classifier Corpus F1pos F1neg F1neutral F1 F1-3
Training Test
1 GerVADER - SB10k 43,54% 35,30% 40,69% 39,42% 39,87%
2 GerVADER - SB10k (NN) 74,50% 53,73% - 64,12%
3 GerVADER - SB10k (10%) 44,52% 37,64% 42,01% 41,08% 41,39%
4 GerVADER - SB10k (10%, NN) 73,15% 55,23% - 64,19%
5 SVM SB10k SB10k (10%) 66,16% 47,80% 81,32% 56,98% 65,09%
6 CNN SB10k SB10k (10%) 71,46% 58,72% 81,18% 65,09% 70,45%
7 SVM MGS SB10k (10%) 67,77% 53,23% 80,20% 60,50% 67,07%
8 CNN MGS SB10k (10%) 63,94% 58,21% 70,66% 61,08% 64,27%
9 GerVADER - SportNews 85,41% 55,05% 12,71% 70,23% 51,06%
10 GerVADER - SportNews (NN) 88,07% 57,78% - 72,93%
11 GerVADER - SportNews (NN, 90,72% 57,78% - 74,25%
N merged into P)
12 GerVADER - News Apps 80,63% 58,14% 11,78% 69,39% 50,18%
13 GerVADER - News Apps (NN) 83,73% 60,75% - 72,24%
14 GerVADER - News Apps (NN, 85,77% 60,75% - 73,26%
N merged into P)
(see Table 2, No. 2+4). Remember however that GerVADER is still rating some
tweets as neutral, so this classification option is still enabled. Only the neutral
statements have been filtered out, the process has been kept the same.
Fig. 2. GerVADER detailed results (see Table 2, No. 1)
Comparing the results with the results by the SB10k authors, one can see
that GerVADER does not outperform any classifier (see Table 2, No. 5-8). We
have to take into consideration however, that the original test corpus is not
publicly available, therefore one can question the results. Nonetheless classifiers
that have not trained on the SB10k corpus still reach 20% better results.
In another test GerVADER has been tested on some parts of the SCARE
corpus (see Table 2, No. 9-14). Tests have been run with reviews referring to news
10 K. M. Tymann et al.
and sport news apps. Since the reviews are given with a comment as well as with
a star rating, the structure of the reviews had been altered. 1-2-star reviews are
interpreted as negative reviews, 3 as neutral and 4-5 as positive reviews.
With the sport news apps results you can see that GerVADER classifies 70%
correctly. Especially the F1 score for positive labels is very good with more than
85% (see Table 2, No. 9). If we sort out the neutral reviews before classifying
it, meaning that we just classify 1, 2, 4 or 5-star ratings, so either negative or
positive labeled reviews, the F1 score raises to 72% (see Table 2, No. 10). Again,
GerVADER still rates reviews as neutral. If we now assume that neutral reviews
are always positive, since one can suggest that a user would rather review an
app that he likes than one he dislikes, we can merge the neutral numbers into
the positive ratings in both labeling cases. We then achieve a F1 score of 74,25%
with an F 1pos score of 90,72% (see Table 2, No. 11).
For the news apps almost equal results have been achieved with the same 3
classification tests (see Table 2, No. 12-14).
In summary one can see that the classifications differ from domain to domain.
Comparing with the original VADER classification the F1 scores are significantly
lower for multiple reasons that will be discussed in the next chapter.
5 Discussion and future work
Although GerVADER has been tested in domains that VADER achieved its best
results, the tests show in some regards rather bad scores. The reason why the
overall F1 scores are that low is the classification of neutral and negative texts.
Concerning the negative texts, one has to ask why so many of the data is
labeled as positive. Actual negative texts are almost as often rated positive as
they are also rated negative. Additionally, also the neutral prediction is in many
tests almost as high as both the negative and positive classification. Therefore
the F 1neg score for negative texts is in all tests much lower than the F 1pos score.
The reason why this is the case is how in German sentences the negation word
”nicht” can often occur at the end of the sentence. It can be very common that
the negation is at the end of the sentence (after the verb) while in English the
negation word is always in pair with the verb(s).
GerVADER for example does not detect the difference between the following
sentences:
1. Ich mag das. (meaning: I like it - i like it)
2. Ich mag das nicht. (meaning: I don’t like it - i like it not)
In both cases GerVADER detects the word ”mag” as a positive word and
therefore calculates the overall sentiment as positive. While the negation word
”nicht” is detected, the sentiment is not shifted. The reason is that only the
sentiment ratings after the negation word are influenced. So, if the negation
word appears at the end of the sentence it has no impact on the overall sentiment.
This is a flaw in the current status of GerVADER that needs to be addressed.
Such an adjustment however requires an overhaul of the algorithm in order to
GerVADER 11
be more suitable for the German language. Because English does not have such
negated sentences, such a logic is not implemented in VADER. If GerVADER
detects the negation words at the end of the sentences, the classification scores
will most likely increase for negated sentences. If this is the case, the overall
F1 score will also improve. Therefore, one can conclude that GerVADER in its
current state biggest flaw is the detection of negative sentences.
Moreover, VADER and therefore GerVADER in its current state have prob-
lems with the negation of longer sentences, even if the negation word is at the
beginning of the sentence. For example, the following sentence is wrongly rated:
1. Ich finde nicht, dass diese Menschen wirklich freundlich sind. (rated posi-
tive, should be negative)
While the negation word ”nicht” is recognized, there are no following words
with a sentiment rating except for the word ”freundlich” (rated positive). But
the occurrence of the word is too far away from the negation word to shift the
sentiment of the word. So, for longer sentences the words following the negation
word are not shifted if there are too many words in between. In combination with
the already mentioned problem with negation words, this gives more insight why
many negative texts are falsely rated positive.
Other than that, GerVADER also needs some other improvements. The
booster and negation words are only translated from the English original. It
lacks a real adaptation for the language. Additionally, there are no phrases cov-
ered. Therefore, phrases like ”Alles in Butter” are not detected. If you split up
the phrase there are no words with a sentiment rating, but read as a phrase it
has a positive meaning (meaning ”all right”).
Furthermore, the lexicon might need more words. Especially German slang
words and words that are commonly used in social media texts might be lacking.
Moreover, the rating process for the current lexicon could be continued, since a
larger crowd promises more trustworthy results.
Lastly a wider benchmark might show better how useful GerVADER in its
present state really is.
6 Conclusion
This paper has shown that GerVADER has potential to become another useful
tool in the sentiment analysis for the German language. While the results com-
pared to VADER are kind of underwhelming, GerVADER promises many areas
for improvement. Given how little the algorithm has actually been altered, the
results for the app reviews clearly show that even in its current form it already
achieves remarkable results. Just like other lexicon approaches, the lexicon can
be easily expanded. Since no machine learning is needed, GerVADER can be
basically used plug-and-play and achieves much faster results without needing
any sort of training. This also results in a consistent rating, meaning that a text
will not be rated differently, where unlike in machine learning approaches the
12 K. M. Tymann et al.
text might be classified differently depending on the training data. If the pro-
posed adjustments are made, GerVADER might be one of the most viable tools
for classifying sentiments. In any case its speed and consistency are two of its
biggest strengths, plus the lexicon can be useful for other researches. Given how
well the role model performs, there is no reason to doubt that GerVADER will
achieve comparable results in the future. GerVADER is publicly available and
will be further worked on [12].
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