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What Causes Wrong Sentiment Classifications of Game Reviews?

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Sentiment analysis is a popular technique to identify the sentiment of a piece of text. Several different domains have been targeted by sentiment analysis research, such as Twitter, movie reviews, and mobile app reviews. Although several techniques have been proposed, the performance of current sentiment analysis techniques is still far from acceptable, mainly when applied in domains on which they were not trained. In addition, the causes of wrong classifications are not clear. In this paper, we study how sentiment analysis performs on game reviews. We first report the results of a large scale empirical study on the performance of widely-used sentiment classifiers on game reviews. Then, we investigate the root causes for the wrong classifications and quantify the impact of each cause on the overall performance. We study three existing classifiers: Stanford CoreNLP, NLTK, and SentiStrength. Our results show that most classifiers do not perform well on game reviews, with the best one being NLTK (with an AUC of 0.70). We also identified four main causes for wrong classifications, such as reviews that point out advantages and disadvantages of the game, which might confuse the classifier. The identified causes are not trivial to be resolved and we call upon sentiment analysis and game researchers and developers to prioritize a research agenda that investigates how the performance of sentiment analysis of game reviews can be improved, for instance by developing techniques that can automatically deal with specific game-related issues of reviews (e.g., reviews with advantages and disadvantages). Finally, we show that training sentiment classifiers on reviews that are stratified by the game genre is effective.
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1
What Causes Wrong Sentiment Classifications of
Game Reviews?
Markos Viggiato, Dayi Lin, Abram Hindle, and Cor-Paul Bezemer
Abstract—Sentiment analysis is a popular technique to identify
the sentiment of a piece of text. Several different domains
have been targeted by sentiment analysis research, such as
Twitter, movie reviews, and mobile app reviews. Although several
techniques have been proposed, the performance of current
sentiment analysis techniques are still far from acceptable, mainly
when applied in domains on which they were not trained. In
addition, the causes of wrong classifications are not clear. In
this paper, we study how sentiment analysis performs on game
reviews. We first report the results of a large scale empirical
study on the performance of widely-used sentiment classifiers
on game reviews. Then, we investigate the root causes for the
wrong classifications and quantify the impact of each cause
on the overall performance. We study three existing classifiers:
Stanford CoreNLP,NLTK, and SentiStrength. Our results
show that most classifiers do not perform well on game reviews,
with the best one being NLTK (with an AUC of 0.70). We also
identified four main causes for wrong classifications, such as
reviews that point out advantages and disadvantages of the game,
which might confuse the classifier. The identified causes are
not trivial to be resolved and we call upon sentiment analysis
and game researchers and developers to prioritize a research
agenda that investigates how the performance of sentiment
analysis of game reviews can be improved, for instance by
developing techniques that can automatically deal with specific
game-related issues of reviews (e.g., reviews with advantages
and disadvantages). Finally, we show that training sentiment
classifiers on reviews that are stratified by the game genre is
effective.
Index Terms—Natural language processing, Sentiment analy-
sis, Computer games, Steam.
I. INTRODUCTION
Sentiment analysis is a widely adopted Natural Language
Processing (NLP) technique to obtain the sentiment (expres-
sion of positive or negative feeling) from text data [29, 39].
This technique consists of identifying the sentiment that
is present in a piece of text (words, sentences, or entire
documents), which corresponds, in its most basic form, to
finding whether the text has a positive, neutral, or negative
sentiment [29]. Sentiment analysis is a research topic that has
gained attention and has presented improvements [15, 49, 56],
being developed and applied in several different domains,
such as Twitter tweets [3, 5, 8], movie reviews [49], cus-
tomer reviews of mobile applications [19, 40], video game
Markos Viggiato and Cor-Paul Bezemer are with the Analytics of Software,
GAmes And Repository Data (ASGAARD) Lab, University of Alberta,
Edmonton, AB, Canada. Email: viggiato@ualberta.ca, bezemer@ualberta.ca.
Dayi Lin is with the Centre for Software Excellence, Huawei, Canada.
Email: dayi.lin@huawei.com. This work is not related to his role at Huawei.
Abram Hindle is with the Department of Computing Science. University
of Alberta, Edmonton, AB, Canada. Email: abram.hindle@ualberta.ca
reviews [50, 52], and various aspects of software develop-
ment [24, 27, 28, 38, 44]. Sentiment analysis is valuable
for game developers because it allows them to capture how
players feel about the game and learn about previous games’
success or failure factors [50]. This knowledge can help game
developers improve their game development processes and
guide them in future releases of their game (e.g., by focusing
on features that users are more positive about).
Several studies have been published on sentiment analysis
with the purpose of developing new techniques, improving cur-
rent techniques, or applying current techniques and classifiers
to existing datasets [11, 22, 24, 27, 28, 29, 50, 52]. However,
the performance of such techniques is still far from acceptable,
mainly when off-the-shelf sentiment analysis classifiers are
applied out of domain, i.e., a classifier is trained in one domain
and applied in a different domain without any configuration
or adjustment. Normally, sentiment analysis techniques must
be adapted to the target domain. For instance, Thompson
et al. [52] adapted a sentiment analysis technique that was
initially designed for movie reviews to be used in video game
chat messages. Despite the low performance of sentiment
analysis, no study has investigated the reason(s) for the low
performance.
In this study, we investigate how different sentiment classi-
fiers perform on game review data. Game reviews from Steam
differ from other types of data to which sentiment analysis is
normally applied. Game reviews contain a more complex text
structure and generally discuss several aspects of the game,
such as the game’s storyline, graphics, audio and controls [58].
Texts from micro-blogging and social media (e.g., Twitter) are
usually very short [17, 36]. In addition, such texts are broader
in scope since they are not necessarily reviewing a game.
Prior work [31] also showed that game reviews are different
from mobile app reviews in several aspects. For instance,
game reviews contain game-specific terminology, which is a
challenge for language processing tools.
Although the diversity of game reviews makes them a rich
source of data, it also poses challenges to NLP techniques,
such as sentiment analysis. For instance, players may mention
the graphical aspects and the storyline of the game in the same
review [50]. The two pieces of text corresponding to such
aspects may have different sentiments, which could confuse
the sentiment classifier when making a classification of the
overall sentiment of the review.
By applying sentiment classifiers on game reviews, we are
able to report the sentiment classification performance and
identify cases where sentiment analysis fails. For instance,
the following review is an example of a difficult classification
2
task for current sentiment classifiers: “Very nice programmed
bugs”. The reviewer makes references to a positive word
(“nice”), with a stronger intensity due to the use of an adverb
(“very”), which might lead the classifiers to classify this
instance as positive. However, the overall sentiment of this
review should be negative as the reviewer is being sarcastic
(the reviewer is pointing out that the game contains bugs).
A deeper investigation of wrong classifications (failing cases)
allows us to find problematic text patterns for sentiment
classifiers and provide insights for game developers about how
to improve the performance of sentiment analysis.
In this paper, we first report the results of a large-scale
empirical study on the performance of sentiment analysis on
12 million game reviews. Our goals are (1) to investigate
how existing sentiment classifiers perform on game reviews,
(2) identify which factors impact the performance and (3)
quantify the impact of such factors. Note that we do not
aim to propose a new sentiment classification technique.
Instead, we investigate reasons for wrong classifications of
existing classifiers. We studied three widely-used and compu-
tationally accessible sentiment classifiers [24, 29]: Stanford
CoreNLP [49], NLTK [7], and SentiStrength [51]. The
selected classifiers adopt different approaches to classify the
text, such as rule and machine learning-based approaches,
which gives more confidence to our study and makes the
results more generalizable.
We evaluated these classifiers on all the game reviews
collected from the Steam platform up to 2016. We then
selected the reviews of which all classifiers misclassified the
sentiment. We manually analyzed a representative and statisti-
cally significant sample of 382 of these reviews to understand
which factors might be causing wrong classifications. Finally,
we performed a series of experiments to quantify the impact of
each identified factor on the performance of sentiment analysis
on game reviews. We address the following three research
questions:
RQ1: How do sentiment analysis classifiers perform on
game reviews?
Investigating the performance of sentiment analysis on game
reviews is the first step to understand how current sentiment
analysis classifiers work on game reviews and whether
they are suitable for this task on such data. We found that
sentiment analysis classifiers do not perform well on game
review data, with AUC values ranging from 0.53 (Stanford
CoreNLP), which is slightly better than random guessing,
up to 0.70 (NLTK).
RQ2: What are the root causes for wrong classifications?
Identifying the causes for wrong classifications contributes
to obtain important insights about how to improve existing
sentiment analysis for game reviews. We found several causes
which mislead the classifiers, such as reviews that make
comparisons to games other than the game under review,
reviews with negative terminology (e.g., reviews that use the
word “kill”) which does not necessarily mean the content has
a negative sentiment, and reviews with sarcasm.
RQ3: To what extent do the identified root causes impact
the performance of sentiment analysis?
Quantifying the impact of each identified root cause to the per-
formance of sentiment analysis is important to support game
developers with the prioritization of causes to be resolved
and a research agenda to address such issues. We found that
reviews which point out advantages and disadvantages of the
game have the highest negative impact on the performance of
sentiment analysis, followed by reviews with game compar-
isons. In addition, we deepened our investigation and showed
that training sentiment classifiers on reviews stratified by the
game genre is effective.
Our study makes three major contributions:
We evaluate the performance of widely-adopted sentiment
analysis classifiers on game reviews from the Steam
platform.
We identify a set of root causes that can explain the wrong
classifications of sentiment analysis classifiers on game
reviews.
We quantify the impact of each identified cause for wrong
classifications on game reviews and provide a research
agenda for addressing these causes.
We provide access to the data1(URLs of game reviews
from Steam with the sentiment classification provided by
all three classifiers).
The remainder of this paper is organized as follows. Sec-
tion II provides a background on sentiment analysis classi-
fication techniques. Section III discusses related work and
Section IV presents the proposed research methodology. Sec-
tion V discusses the pre-study. In Sections VI, VII, and VIII,
we discuss the results, while in Section IX we present our
recommendations on how to perform sentiment analysis on
game reviews. Finally, Section X concludes our paper.
II. SENTIMENT ANA LYSIS
In this section, we present an overview of the main senti-
ment analysis techniques along with the most used classifiers
that adopt these techniques. In this work, we use ‘technique’
to refer to the method adopted for the sentiment classification
and ‘classifier’ (which can also be understood as ‘tool’ or
‘framework’) to refer to an implementation of a technique
(i.e., an actual instance of the technique). Next, we discuss
each technique and the representative classifier(s) we chose
for our work. For this study, we focus on popular, open source
and free-to-use sentiment analysis classifiers.
Sentiment analysis techniques are responsible for identify-
ing the sentiment present in a piece of text, which can be
either positive, neutral, or negative [29, 39]. Table I presents
an overview of the main sentiment analysis techniques and
classifiers which have been proposed in prior studies. This
is not an exhaustive list of sentiment classifiers and it com-
prehends the most reported classifiers in prior studies. The
grouping of classifiers under a specific technique category was
done based on the method the classifier uses. Classifier names
in bold refer to the ones studied in this work. The last column
1https://github.com/asgaardlab/sentiment-analysis-Steam reviews
3
shows the type of data on which the classifier was originally
trained. Next, we detail each technique and the corresponding
classifier(s) we chose to use in our study.
1) Machine Learning-based Techniques: Machine learning-
based classifiers leverage machine learning algorithms, such as
Support Vector Machines, Na¨
ıve Bayes, and Neural Networks.
Examples of classifiers that adopt this technique are NLTK [7],
Stanford CoreNLP [49], and Senti4SD [10]. For our
study, we selected NLTK and Stanford CoreNLP, which
are open source, free to use and very popular [24, 28].
NLTK is part of a larger NLP package that provides many
other functions.2Regarding sentiment analysis, NLTK uses a
bag of words model. In order to apply NLTK, we can adopt
two different approaches: train a Na¨
ıve Bayes classifier on
our data and apply the built model (as we did) or use the
VADER (Valence Aware Dictionary and sEntiment Reasoner)
model, which was trained on social media texts, such as
micro-blogs [29]. The latter approach provides four scores
for each sentence: compound (varies from very negative to
very positive as indicated by a score in the range [-1, +1]),
negative (probability of being negative), neutral (probability
of being neutral), and positive (probability of being positive).
In the former approach, we train a Na¨
ıve Bayes model to
classify each review (it provides the probability of being
positive). Figure 1a presents examples of reviews classified
by the machine learning version of NLTK. As we can see, the
positive example is correctly classified. However, NLTK is not
able to capture the negative sentiment of the sentence “I am
so happy the game keeps freezing”, which contains sarcasm.
Stanford CoreNLP was developed by the Stanford
Natural Language Processing Group3at Stanford University.
The authors propose a model called Recursive Neural Tensor
Network, of which the implementation is based on a Recurrent
Neural Network (RNN). The technique consists of parsing the
text to be classified into a set of sentences and performing a
grammatical analysis to capture the compositional semantics
of each sentence [27, 29, 49]. Then, a score between ‘0’
and ‘4’ is assigned for each sentence, in which ‘0’ means
avery negative sentiment, ‘1’ means negative, ‘2’ refers to a
neutral sentiment, and ‘3’ and ‘4’ refer to positive and very
positive sentiments, respectively. To classify a game review
(composed of more than one sentence), we adopt the following
approach [28]: -2*(#0) - 1*(#1) + 1*(#3) + 2*(#4), in which
#0 refers to the number of sentences with score 0, and so on.
If the resulting score is above zero, the review sentiment is
positive; if it is below zero, the review sentiment is negative;
otherwise, the review sentiment is neutral.
In Figure 2, we can see an example of how the sentence
I killed the evil enemy and I won”, which is positive,
is wrongly classified using Stanford CoreNLP (the root
node indicates it is a negative sentence). As we can observe,
each node in the parse tree is assigned a score (from negative
to neutral to positive) and the final sentiment is obtained
via the compositional structure of the tree. We can see that
different nodes are assigned different sentiments (relative
2https://www.nltk.org/
3https://nlp.stanford.edu/
to the partial sentence composed up to that node) and the
sentiment contained in the root is supposed to capture the
overall sentiment of the full sentence, which is opposed to
only inspecting the sentiment of each word individually and
summating the scores. This example was obtained from the
Stanford CoreNLP sentiment analysis website with the
live demo tool.4
2) Rule-based Techniques: Rule-based classifiers are based
on a predefined list of words along with their sentiment score.
The piece of text is split into words, and the scores of each
word are composed into a final score for the entire piece. Ex-
amples of rule-based classifiers are SentiStrength [51],
SentiStrength-SE [24], and EmoTxt [9]. In this work,
we apply SentiStrength, which is of one of the most used
sentiment classifiers across different domains, such as social
media (e.g., Twitter) [2], and movie reviews [35].
SentiStrength is a rule-based classifier to classify
sentences into sentiments based on a word bank in which
each word has a sentiment score associated with it (this is
also called lexical analysis). This classifier is based on a
model trained on the MySpace social media network [29]. The
document under analysis must be tokenized into sentences,
which are assigned two scores based on the summation of
each word’s score: a positive strength score (how positive is
the text) that ranges from 1 (not positive) to 5 (very positive),
and a negative strength score (how negative is the text) that
ranges from -1 (not negative) to -5 (very negative).
Figure 1b presents some examples of classifications made
by SentiStrength. We can see that the classifier’s ap-
proach of getting the sentiment score of each word individually
and summating the scores does not work for some cases. The
classifier is not able to capture the negative sentiment in the
sentence “I am so happy the game keeps freezing”, which is
sarcastic. This possibly happens due to the presence of the
word “happy”, which is a positive word and misleads the
tool to classify the whole sentence as positive. In addition,
SentiStrength is not able to capture the neutral sentiment
in the sentence “The game was nothing special”, possibly
due to the presence of the positive word “special”, which is
positive.
III. REL ATED WORK
In this section, we describe prior work on the application of
sentiment analysis on game data and on other types of data.
We also discuss empirical studies on game reviews. Note that,
in our work, we do not aim at proposing a new sentiment
analysis technique. Instead, we investigate the performance
of existing sentiment classifier on game reviews and reveal
the root causes for wrong classifications. We focus on popular
sentiment classifiers, which are not computationally expensive
(e.g., deep learning-based classifiers).
Sentiment Analysis on Game Data and Reviews. Thompson
et al. [52] studied how to extend a lexicon-based sentiment
analysis technique for the purpose of analyzing StarCraft 2
player chat messages. The authors updated the entries to the
4http://nlp.stanford.edu:8080/sentiment/rntnDemo.html [Accessed online:
March 11th, 2020]
4
TABLE I: Sentiment analysis techniques, corresponding classifiers and default training dataset.
Technique Classifier Default training dataset Used by
Machine learning
NLTK*[7] Micro-blog texts [27], [28], [29], [41], [34]
Stanford CoreNLP [49] Movie reviews [27], [28], [42], [33], [55]
IBM Alchemy** [27], [28], [48], [6]
Senti4SD [10] Stack Overflow posts [10], [25]
Rule-based
SentiStrength [51] MySpace [20] [22], [21], [27], [28]
SentiStrength-SE [24] JIRA [24], [25]
EmoText [9] Stack Overflow, JIRA [9], [25], [37]
*Note that we use the machine learning version of NLTK instead of its VADER version (which uses a rule-based
approach).
** IBM Alchemy is available as a service within IBM Watson at https://www.ibm.com/watson/services/
tone-analyzer/.
True sentiment
NLTK classification
Sentence
Negative
Positive
I am so happy the game keeps freezing
Positive
Positive
Was blown away by some of the developments in the story in this game,
not
gonna spoil but def a must try
(a) Example of classifications made by NLTK.
True sentiment
SentiStrength classification
Sentence
Negative strength
Negative
Positive
I am so happy the game keeps freezing
2-1
Neutral
Positive
The game was nothing special
2-1
Positive
Negative
Was blown away by some of the developments in the story in this game,
not
gonna spoil but def a must try 1-2
(b) Example of classifications made by SentiStrength.
Fig. 1: Examples of sentiment classifications.
-
-
+
I
killed
the evil enemy
and I
won
-
Fig. 2: Example of the Recursive Neural Tensor Network
predicting the sentiment in a sentence.
word dictionary and tailored it to the gaming context. The
approach was able to classify sentiment and identify toxicity
of instant messages across 1,000 games. The best fitting model
outperformed the baseline (which predicts that every message
has a positive sentiment) for the sentiment classification.
The authors also performed a niche analysis, which showed
that the model performances remained relatively stable across
regions, leagues, and different message lengths. Str˚
a˚
at and
Verhagen [50] investigated user attitudes regarding previously
released video games. The authors performed a manual aspect-
based sentiment analysis on all user reviews from two game
franchises: the PC-version of three games from the Dragon
Age franchise and the three first games from the Mass Effect
franchise. The data was collected from the Metacritic platform.
The paper showed that the rating of a user review highly
correlates with the sentiment of the aspect in question, in
the case of a large enough data set. Zagal et al. [59] studied
397,759 game reviews to identify the sentiment of 723 adjec-
tives used in the context of video games. The authors found
that some words which are generally used with a negative (or
positive) connotation have a positive (or negative) connotation
in the game domain. Finally, Chiu et al. [13], Raison et al.
[43], Wijayanto and Khodra [53], and Yauris and Khodra [57]
analyzed the sentiment about specific aspects of the game
(such as graphics and storyline) in reviews. For example, take
the following sentence: “An okay game overall, good story
with very bad graphics”. The player has a positive feeling
about the game’s storyline, but a negative feeling about the
game’s graphics. An aspect-based sentiment analysis would
compute a different sentiment score for each mentioned aspect.
The aforementioned leveraged different approaches (e.g.,
lexicon-based and aspect-based) to perform sentiment analysis
on different types of data. In contrast, we evaluate existing
sentiment analysis techniques on game reviews, identify the
causes for misclassifications, and quantify the impact of those
causes in the performance of the classifiers.
Sentiment Analysis on Other Types of Data. Agarwal et al.
[1] built different types of models (a feature based model and
a tree kernel based model) to perform two classification tasks
using Twitter data: a binary task to classify tweets into positive
and negative classes; and a 3-way task to classify tweets into
positive, negative, and neutral classes. The authors showed
that both models outperform the state-of-the-art approach by
5
then, which consisted of a unigram model. The proposed
models presented a gain of 4% in performance in comparison
to the baseline. Saif et al. [46] also used Twitter-related
data to build sentiment analysis models. The authors added
semantic features into the three different training datasets: a
general Stanford Twitter Sentiment (STS) dataset, a dataset on
the Obama-McCain Debate (OMD), and one on Health Care
Reform (HCR). The results showed that combining semantic
features with word unigrams outperforms the baseline (only
unigrams) for all datasets. On average, the authors increased
the accuracy by 6.47%.
Lin and He [30] proposed a probabilistic modeling frame-
work based on Latent Dirichlet Allocation (LDA) to detect
sentiment and topic at the same time from a piece of text.
The authors evaluated the model on a movie review dataset
and they only consider two classes: positive and negative.
The results showed that the proposed approach obtained an
accuracy of 84.60%, outperforming some state-of-the-art ap-
proaches. Guzman and Maalej [20] proposed an automated
approach to analyze mobile app reviews. The authors used the
NLTK classifier to identify fine-grained app features in the user
reviews. They obtained the sentiment of these features and
used topic analysis to group them into higher-level groups.
The authors used 7 apps from the Apple App Store and
Google Play Store and their approach presented a precision
of 59% and a recall of 51%. Rigby and Hassan [44] used a
psychometrically-based linguistic analysis tool called Linguis-
tic Inquiry and Word Count (LIWC) to examine the Apache
httpd server developer mailing list. The authors assessed the
personality of four top developers, including positive and
negative emotions present in the mailing list. Among the
results, the authors found out that the two developers that
were responsible for two major Apache releases had similar
personalities, which were different from other developers on
the traits of extroversion and openness. Bazelli et al. [4] ana-
lyzed StackOverflow posts to identify and compare developers’
personality types. They also used the LIWC tool. The results
show that, compared to medium and low reputed users, top
reputed post’s authors are more extroverted, indicating the
presence of social and positive LIWC measures as well as
the absence of tentative and negative emotional measures.
In addition, authors of up voted posts present less negative
emotions than authors of down voted posts.
The aforementioned works used data from three different
sources: Twitter, movie reviews, and mobile app reviews. On
the other hand, we focus on game reviews from a digital
distribution platform (Steam).
Studies on Game Reviews. Zagal et al. [58] analyzed
and characterized game reviews from different websites. The
authors used open coding to come up with the topics present
in the reviews. Their findings show that game reviews are rich
and varied in terms of themes and topics covered. For instance,
players might post descriptions of the game under review,
their personal experience, advice to other players who read
the review, and suggestions for game improvements. Zagal
and Tomuro [60] performed a study on a large body of user-
provided game reviews aiming at comparing the characteristics
of the reviews across two different cultures. The authors
collected reviews from Famitsu and Game World (Japanese
gaming websites) and from Gamespot and Metacritic (US
gaming websites). Among the findings, the authors mention
that American players value the replay of a game, while
Japanese players are more strict towards bugs. The works
mentioned above studied the characteristics of game reviews
and what are the differences between reviews from different
cultures. Differently, on our work, we use game reviews for the
purpose of evaluating existing sentiment classifiers and come
up with the causes for wrong classifications.
As we can see, all the aforementioned works proposed new
sentiment analysis models and explored the characteristics
of game reviews with regard to several different aspects.
However, we still lack clarification regarding the performance
of existing sentiment classifiers on game reviews, which game
review text characteristics impact the performance of senti-
ment analysis and to what extent they impact it. In our study,
we perform a large-scale study with more than 12 million
reviews from Steam to evaluate existing sentiment classifiers
and reveal text characteristics which are problematic for these
classifiers.
IV. METHODOLOGY
In this section, we detail the methodology that is used in
our study to evaluate existing sentiment classifiers on game
reviews from Steam and identify the root causes for wrong
classifications. Figure 3 presents a complete overview of our
methodology, which is detailed next.
Evaluating Sentiment Analysis Performance
Stanford
CoreNLP Run classifier
on x reviews
SentiStrength
Sample x
reviews Training set
Out-of-sample
bootstrap
Process
output
NLTK Train model
Evaluation
metrics
Evaluation
metrics
Run classifier
on x reviews
Process
output
Evaluation
metrics
Testing set
Sample another
x reviews Built model
Collecting Game Reviews
Extract game
reviews Game reviews
Steam Community
Manually Analyzing Wrong Classifications
Manual analysis of
reviews Root causes
Reviews wrongly
classified
Quantifying the Impact of the Root Causes
Select reviews with
and without the
root causes
Evaluate NLTK on
the two review sets
Compare the
evaluation
metrics
Fig. 3: Study methodology overview.
A. Collecting Game Reviews
We collected the reviews of all 8,025 games that were
available in the Steam Store on March 7th, 2016 using a
customized crawler. We removed games that had less than 25
reviews from our initial dataset to reduce a possible bias in
our results due to a small number of reviews (e.g., because a
large portion of those reviews were posted by friends of the
developers). In total, we collected reviews of 6,224 games.
6
We extracted all the reviews for each game from the Steam
Community and ended up with a total of 12,338,364 reviews
across all supported natural languages. Steam provides a filter
for the language of reviews for a game. We crawled the reviews
in each language separately using this filter, to identify the
language of each review. Most reviews are written in English
(6,850,130), but there are also reviews in Russian (1,789,979),
German (525,548), Spanish (469,582), Portuguese (441,145),
and French (396,057), since the classifiers can handle several
different languages. Besides the review itself, we also collected
other available data: the recommendation flag (i.e., whether the
reviewer recommended the game or not), early access status
(i.e., whether a game is in the early access stage or not), the
number of playing hours, the author ID, the date when the
review was posted, helpful count, not helpful count, funny
count, and the URL of the review.
Note that our data consists of Steam game reviews, which is
different from Metacritic reviews. The Metacritic website5ag-
gregates game reviews from professionals and amateurs. While
amateur reviews have similarities with Steam reviews (e.g.,
Metacritic amateur reviews contain gameplay and experience
descriptions), professional reviews are much longer and more
complex [47]. Therefore, further investigation is necessary to
properly assess whether the sentiment classifiers adopted in
our work can be applied to professional Metacritic reviews.
B. Evaluating Sentiment Analysis Performance
We evaluated the performance of three sentiment anal-
ysis classifiers on game reviews, namely Stanford
CoreNLP (version 3.9.2) [49], NLTK (version 3.4) [7], and
SentiStrength (Windows version) [51]. For the purpose
of evaluation of the classifiers, we consider the game rec-
ommendation flag on Steam as the sentiment truth label in
our data, that is, we make the assumption that a review that
recommends a game has a positive sentiment, while a review
that does not recommend a game has a negative sentiment. Our
dataset contains 10,603,348 positive reviews (recommendation
= 1) and 1,735,016 negative reviews (recommendation = 0).
Although our dataset is imbalanced, we do not fix the
imbalance since our goal is to evaluate existing techniques
and reveal the root causes for wrong classifications rather than
proposing a new sentiment analysis technique that outperforms
the state-of-the-art. Furthermore, in the real world, data distri-
bution is often imbalanced [12, 56] and existing re-sampling
techniques have serious defects for text data [56]. Finally, we
adopt the Area Under the Receiver Operating Characteristic
Curve (AUC) evaluation metric, which is a robust metric with
imbalanced data [23].
Regarding NLTK, we have the option to train it on our own
data using the Na¨
ıve Bayes algorithm. Since it is computa-
tionally expensive to train and test it on our entire data, we
adopt the out-of-sample bootstrap technique [16] to perform
the training and testing, since the use of this technique allows
us to avoid possible bias in the training and testing sets as we
would have with a simple one-time sampling. In our work,
the out-of-sample bootstrap technique consists of randomly
5https://www.metacritic.com/
sampling 100K reviews (sample) with no replacement from
the entire set of reviews (population) to train the classifier.
Then, we randomly select another 100K reviews from the pool
of remaining reviews to test the classifier. The sample size
(100K) was appropriately determined in a pre-study (detailed
in Section V). The bootstrap process is repeated 1,000 times,
which is enough to represent the entire population and reduce
a possible bias in the training and testing sets. Note that for
all executions of NLTK, before performing the classification
itself, we have a preprocessing pipeline, which consists of
tokenization, case normalization, and stop word removal. For
the SentiStrength and Stanford CoreNLP classifiers,
we also adopted the bootstrap technique and evaluated them
on the same 1,000 samples used to test NLTK. Note also that
we opted for not changing the configurations of the ready-to-
use classifiers, such as SentiStrength and Stanford
CoreNLP, as prior work has mostly used them without
changes to their configuration [20, 27, 28, 29]. Keeping
the configuration of classifiers similar to the configuration
previously used allows us to evaluate and compare our results
with existing literature more fairly.
For all the classifiers, we computed the Area Under the Re-
ceiver Operating Characteristic Curve (AUC). With the boot-
strap process, we are able to obtain the AUC distribution for all
the classifiers (1,000 AUC values corresponding to the 1,000
bootstrap iterations). The Receiver Operating Characteristic
Curve plots the true positive rate against the false positive rate.
The AUC measures the classifier’s capability of distinguishing
between positive and negative sentiments and ranges from 0.5
(random guessing) to 1 (best classification performance). For
the cases in which the classification is neutral, we always
consider it as a wrong classification since our data has only
two labels: positive (the reviewer recommends the game) and
negative (the reviewer does not recommend the game).
We compared the AUC distributions using the Wilcoxon
rank-sum test. The Wilcoxon rank-sum test is an unpaired,
non-parametric statistical test, where the null hypothesis is
that two distributions are identical [54]. If the p-value of the
applied Wilcoxon test is less than 0.05, then we can refute
the null hypothesis, which means that the two distributions
are significantly different. In addition to checking whether the
two distributions are different, we provide the magnitude of
the difference between the two distributions using Cliff’s delta
d[32] effect size. We adopt the following thresholds for d[45]:
Effect size =
negligible(N),if |d| 0.147
small(S),if 0.147 <|d| 0.33
medium(M),if 0.33 <|d| 0.474
large(L),if 0.474 <|d| 1
C. Manually Analyzing Wrong Classifications
To understand why classifiers are making wrong classifica-
tions and come up with the root causes which might be leading
to the poor classification performance, we performed a manual
analysis on the reviews that were wrongly classified by each
of the three sentiment analysis classifiers we use. With this
approach, we are more likely to identify characteristics from
7
the review text itself that might confuse the classifier rather
than wrong classifications due to bias in a classifier.
We adopt an inductive approach similar to the open-coding
technique [14] to manually analyze the reviews. Initially, two
authors independently read 100 reviews, being 50 wrongly
classified as positive and 50 wrongly classified as negative.
They then came up with causes that might have misled the
classifiers. After discussing these causes and reaching an
agreement on four causes (plus two categories in which the
misclassification was unclear), we selected a representative
sample with a confidence level of 95% and a confidence
interval of 5%, which corresponds to 382 reviews. This sample
was then classified into the set of agreed upon causes by one
author so we could obtain the percentage of reviews for each
cause.
D. Quantifying the Impact of the Root Causes
Based on the previous step, in which we extracted possible
causes for wrongly classified reviews, we conducted a series
of experiments to evaluate the impact of the identified causes,
separately, on the performance of the sentiment analysis clas-
sifiers. For each cause, we selected the set of reviews that
are affected by that cause (the affected set), the set of the
remaining reviews (the unaffected set), computed the AUC
distribution for both sets, and compared the AUC distributions
using the Wilcoxon rank-sum test and the Cliff’s delta effect
size.
V. PRE-STUDY
We need to find the best sample size to train the NLTK clas-
sifier. In this section, we present our pre-study to investigate
the performance of NLTK with different sample sizes to train
and test it.
Training NLTK on our entire dataset would be computation-
ally expensive. We designed two experiments to determine
the proper training and testing set sample sizes so we can
apply the out-of-sample bootstrap technique, as we explained
in Section IV-B. In Figure 4, we can see the plots regarding
our experiments. For both cases, we used the following values
for the sample size (number of reviews): 1K, 10K, 100K, and
200K. Figure 4a presents how the training time (in hours)
varies with the sample size. As we can see, the time increases
quickly with the increase in sample size (jumping from 26
hours, for 100K, to 75 hours of training time, for 200K).
Therefore, a sample size larger than 200K would be infeasible.
Figure 4b presents how the performance of the NLTK
classifier (by means of the median AUC) varies with the
increase in the sample size. As we can observe, the plot
plateaus when it reaches 100K (presenting an AUC of 0.67),
which means using 100K reviews is sufficient for our purpose.
Using the result of this experiment together with the result of
the previous experiment, we decided to use a sample of 100K
game reviews to train and test the NLTK classifier. We also
used the same sample size to evaluate the SentiStrength
and the Stanford CoreNLP classifiers.
These results provide evidence of the richness of game
review data as we do not need the entire dataset to train our
Sample size
Training time (hours)
0 10 30 50 70
0 50K 100K 150K 200K
(a) Sample size versus training
time.
Sample size
Performance (AUC)
0 0.62 0.64 0.66
0 50K 100K 150K 200K
(b) Sample size versus AUC.
Fig. 4: Plots of experiments to determine the sample size for
NLTK.
model, indicating that, although the sentiment classification is
a tricky problem, we have a rich dataset for which the model
does not need huge amounts of data to learn from.
VI. RQ1: HOW D O SE NT IM EN T ANALYS IS C LA SSIFIERS
PERFORM ON GAME REVIEWS?
Motivation: It is important to verify the performance of
widely-used sentiment analysis classifiers on game reviews as
this is the first step to understand whether current sentiment
analysis classifiers are suitable for classifying the sentiment of
such data.
Approach: For this research question, we applied the out-of-
sample bootstrap with 1,000 iterations to evaluate the NLTK,
Stanford CoreNLP and SentiStrength classifiers on
the game review. To evaluate the classifiers, we computed
five metrics: accuracy, precision, recall, F-measure, and AUC.
We also performed an experiment to investigate how the
length of the reviews affects the performance of the sentiment
classification. The reviews were split into 51 groups according
to their length: reviews with less than 20 characters, reviews
with length between 20 and 40 characters (exclusive), reviews
with length between 40 and 60 characters (exclusive), and
so on up to the last group of reviews with more than 1,000
characters. We evaluated each classifier with a sample of 10K
reviews from each length range. Finally, we compared the
performance of the sentiment classification of game reviews
with the sentiment classification of other three corpora (Stack
Overflow posts, Jira issues, and mobile app reviews), as
indicated by prior work [26, 29].
Findings: Table II presents the metrics for the imbalanced
and balanced versions of the dataset. Note that we provide
all these metrics for the purpose of comparisons with prior
(and future) work, but for our discussions, we will focus on
the AUC metric. NLTK achieved the best performance of
sentiment analysis (in the studied configuration) on game
reviews while Stanford CoreNLP presented the worst
performance. Figure 5 presents the distribution of the AUC
metric for the classifiers (each value corresponds to an iteration
of the bootstrap).
8
TABLE II: Evaluation metrics (median) for unbalanced and
balanced dataset.
Classifier Acc. Precision Recall F-measure AUC
NLTK 0.61 0.60 0.70 0.54 0.70
NLTK (balanced) 0.67 0.73 0.67 0.65 0.67
SentiStr. 0.52 0.56 0.63 0.47 0.63
SentiStr. (balanced) 0.63 0.65 0.63 0.62 0.63
Stanf. NLP 0.37 0.52 0.53 0.35 0.53
Stanf. NLP (balanced) 0.53 0.54 0.53 0.51 0.53
NLTK SentiStrength Stanf. CoreNLP
AUC
0 0.56 0.6 0.64 0.68 0.72
Fig. 5: AUC distribution for NLTK bootstrap.
The AUC for NLTK varies from 0.69 up to 0.72, with a
median value around 0.70. For SentiStrength, the AUC
ranges from 0.60 to 0.61 with a median of 0.60, while
for Stanford CoreNLP, the AUC ranges from 0.53 to
0.54 with a median of 0.53. For all classifier pairs ([NLTK,
SentiStrength], [NLTK,Stanford CoreNLP], and
[SentiStrength,Stanford CoreNLP]), the Wilcoxon
rank-sum test shows that the two distributions are significantly
different, with a large Cliff’s delta effect size.
0.5
0.6
0.7
0.8
0_20
40_60
80_100
120_140
160_180
200_220
240_260
280_300
320_340
360_380
400_420
440_460
480_500
520_540
560_580
600_620
640_660
680_700
720_740
760_780
800_820
840_860
880_900
920_940
960_980
1000+
Review length (number of characters)
AUC
Classifier
NLTK
SentiStrength
Stanford CoreNLP
Fig. 6: Performance of classifiers for different length ranges.
Note that there is a data point for every range of 20 characters
(0-20, 20-40, and so on). However, for the purpose of a better
visualization, the figure only displays every other range in the
xaxis (e.g., the label ‘20 40’ is not shown in the plot, but the
corresponding data point for that range is present in the plot).
Figure 6 shows that the performance of the classifiers
TABLE III: F-measure of sentiment classification across dif-
ferent corpora.
Corpora NLTK SentiStrength Stanford CoreNLP
Game reviews 0.54 0.47 0.35
Stack Overflow posts 0.21 0.34 0.28
Jira issues 0.55 0.62 0.52
Mobile app reviews 0.53 0.64 0.74
remains mostly stable across different review lengths, with
the largest changes occurring for reviews with less than 20
characters (AUC of 0.65 for NLTK) and reviews with more
than 1,000 characters (AUC of 0.61 for NLTK). We can also
see that NLTK’s performance slightly reduces as the review
length increases. Finally, we computed the distribution of
different review lengths in our dataset. We found that 75%
of the reviews are in the range 20-1000 characters (where
NLTK performs best), while 20% of the reviews have less than
20 characters, and 5% of the reviews have more than 1,000
characters.
Finally, Table III presents the F-measure metric of the
sentiment classification across different corpora. The text in
bold represents a classification performance better than for
game reviews. As we can see, the classifiers usually perform
better when using a corpus other than game reviews. After
training NLTK on game reviews, it achieves a performance
that is similar to the performance on the Jira issues and
mobile app reviews corpora. However, SentiStrength and
Stanford CoreNLP work much better on the Jira issues
and mobile app reviews corpora compared to game reviews.
Overall, sentiment analysis classifiers do not achieve a
high performance, performing worse on game reviews
than on other domains. The median AUC ranged from
0.53 (Stanford CoreNLP) to 0.70 (NLTK).
VII. RQ2: WHAT ARE T HE RO OT CAUSES FOR WRON G
CLASSIFICATIONS?
Motivation: Understanding what is causing sentiment analysis
classifier to make wrong classifications is essential to extract
important insights about how to improve existing sentiment
analysis for game reviews. Such knowledge can be used to
fix problems in the classification pipeline and achieve a better
performance.
Approach: We start by (1) selecting the reviews that were
misclassified by all the three classifiers simultaneously (i.e.,
the intersection of misclassified reviews). We then (2) use
the pool of all misclassified reviews to select a statistically
representative sample of 382 reviews for the manual analysis.
We adopted an open coding-like approach to identify the
root causes which could affect sentiment analysis classifiers’
performance. Two authors independently analyzed a sample
of 100 reviews (50% wrongly classified as positive and 50%
wrongly classified as negative) to identify the root causes
that may confuse the classifiers. Our manual analysis had
an agreement of 83% between the two authors (we consider
9
TABLE IV: Root causes for misclassifications in sentiment analysis (each review may be assigned to more than one root
cause).
Root cause Definition Occurrence (%)
Contrast conjunctions The review points out both the advantages and disadvantages of the game,
frequently using contrast conjunctions 30
Game comparison The review contains a comparison with another game or with a previous version
of the game itself 25
Negative terminology The review contains words such as kill and evil which are not necessarily bad
for specific game genres (e.g., action games) 23
Unclear It is not clear what might have caused the wrong classification 21
Sarcasm The review contains sarcastic text 6
Mismatched recommendation The user might have entered a wrong recommendation: positive (negative)
recommendation with a negative (positive) review content 6
an agreement when both authors agreed that the root cause
X is related to a review Y). After reaching the agreement,
one author analyzed a statistically representative sample of
382 reviews (which yields a confidence level of 95% with
a confidence interval of 5) to compute the frequency of
occurrence of each cause. Note that each misclassification
may be assigned to more than one root cause (if that is the
case). The sample of 382 reviews for the manual analysis was
obtained from the reviews that were misclassified by all three
classifiers. We focused on reviews that were misclassified by
all classifiers to better identify characteristics of the review text
that affect the sentiment analysis classification, rather than a
characteristic of only a single classifier.
Findings: We revealed four types of possible causes for
sentiment misclassifications: use of contrast conjunctions
to indicate the advantages and disadvantages of a game
in the same review, comparison to other games, reviews
with negative terminology, and sarcasm. Table IV presents
all the root causes we identified along with their definitions
and percentage of occurrence. As we can see, the most com-
mon cause is contrast conjunctions (30%), followed by game
comparison (25%), negative terminology (23%), and sarcasm
(6%). Cases for which we are not able to clearly identify the
cause for the wrong classification (unclear) occurred in 21% of
the reviews. Cases in which the review content did not match
the recommendation (mismatched recommendation) occurred
in 6% of the reviews.
Next, we present each root cause in detail along with
corresponding examples of reviews.
Root cause 1: Contrast conjunctions
Description: The review points out advantages and disadvan-
tages of the game.
Symptoms: This type of review frequently makes use of
contrast conjunctions (but,although,though,even though, and
even if ) when presenting positive and negative points about the
game. As we can see in the example below, the review contains
a positive view (“I love this game...”) and a negative view (“...it
keeps flickering please help!”) about the game separated by the
conjunction but.
Example: “I love this game but it keeps flickering, please
help!”.
Root cause 2: Game comparison
Description: The review compares the game with another
game or a previous version of the game itself. Such compar-
isons might make the sentiment classification more difficult
since positive or negative points might refer to the other game
or the game itself in a previous version instead of the current
game version under review.
Symptoms: The review mentions one or more games [A, B...]
in a review for another game [G], or mentions a version 1.x of
the game [G] in a review for the version 2.x of the same game
[G]. In the example below, the review for the Terraria game
compares the reviewed version of the game with a previous
version.
Example: “Terraria was one of the best games I’ve ever
played, but after they released 1.2, I stopped enjoying it!”.
Root cause 3: Negative terminology
Description: The review uses (supposedly) negative terminol-
ogy (i.e., words with a negative connotation), which might
mislead the classifier towards a negative sentiment classifica-
tion even though many times the review text has a positive
sentiment (as indicated by the recommendation of the game).
Symptoms: The review contains words that are considered
negative in many situations (e.g., kill,evil), which might not
have a negative connotation for games of specific genres, such
as first-person shooter games. The review in the example be-
low contains supposedly negative words, such as kill, although
it is just describing the role of the player in the game. In
fact, the reviewer recommended the game and even made it
explicitly by assigning a score of 10 out of 10 to the game.
Example: “I’ve played like 15 games [...], zombies just go
around you, you can’t run, just keep trying to kill them.”.
Root cause 4: Unclear
Description: We are not able to clearly identify a pattern or
characteristic that might be confusing the classifier.
Symptoms: There is no symptom. We cannot identify a clear
possible reason which might mislead the classifier. The review
in the example below was classified as positive while it should
be negative.
Example: “Downloaded Game into steam, Played for 40
Hours total. Game disappeared from computer. Redown-
loaded, Played for a while, Game disappeared again. As
10
someone with a download cap and 2 other gamers in the
house, was. not. impressed”.
Root cause 5: Sarcasm
Description: The review contains sarcastic text. Sarcasm
occurs when an apparently positive text is actually used to
convey a negative attitude (or vice-versa) [18]. Prior work has
shown that sarcasm is difficult to automatically identify [39].
Symptoms: The review contains sarcasm, which is observed
when the reviewer writes an (apparently) positive text intend-
ing to transmit a negative message (or vice-versa). The review
in the example below contains sarcastic text as the reviewer
makes use of positive words (e.g., great), when the person
actually points out a negative aspect about the game.
Example: “Great for uninstalling 11/10 would uninstall
again”.
Root cause 6: Mismatched recommendation
Description: It means the reviewer might have entered a
wrong recommendation, which does not match with the review
content itself. Note that this root cause is different from sar-
casm (as we cannot clearly identify a positive review intending
to transmit a negative attitude or vice-versa), however both
causes are hard to be automatically identified.
Symptoms: The reviewer is positive about the game, but they
did not recommend the game (or vice-versa). The example
below presents a review that was classified as positive (as
expected since the text clearly expresses a positive sentiment).
However, the reviewer did not recommend the game, which
we assume was a mistake of the reviewer.
Example: “I love this GAME!”.
We identified four root causes for wrong classifications of
sentiment analysis classifiers: use of contrast conjunctions
(30%), game comparisons (25%), negative terminology
(23%), and sarcasm (6%).
VIII. RQ3: TO WH AT EXTENT DO THE IDENTIFI ED ROOT
CAU SE S IM PACT T HE PERFORMANCE OF SENTIMENT
ANA LYSIS?
Motivation: It is important to quantify the impact of each
identified root cause to the overall performance of sentiment
analysis on game reviews. Such knowledge will support the
prioritization of the causes to be addressed, the implementation
of better sentiment analysis tools to be deployed in gaming
contexts, and a research agenda to address such issues.
Approach: For this part of the study, we first identified the
root causes which are feasible to be automatically identified
in reviews. Then, we implemented detection heuristics to
identify reviews affected by each root cause. We focused on
the following root causes for which we can automatically
identify reviews: contrast conjunctions,game comparison,
and negative terminology. After identifying such reviews,
we re-ran the NLTK classifier on both groups: the set of
identified reviews (affected set, which is supposedly harder
for the classifier) and the set of remaining reviews (unaffected
set, which is supposedly easier for the classifier since they do
not contain the cause for the wrong classification).
Note that, in this last part, we focused only on the NLTK
classifier as it presented the best performance (Section VI) and
it can be trained on our data. Furthermore, we also applied the
bootstrap technique with 1,000 iterations, as we previously did.
Next, we explain the implemented detection heuristics and
the obtained results for each root cause.
A. Contrast Conjunctions
Detection heuristic: We noticed that reviews which point
out the advantages and disadvantages of a game usually use
contrast conjunctions to transmit the idea of contrast between
advantages and disadvantages of the game. We defined a list
with the contrast conjunctions we observed in our manual
analysis and performed a keyword-based search in our dataset
to identify reviews that contain one or more conjunctions of the
list. Table V presents the selected conjunctions with examples.
Among the most frequent conjunctions found in the reviews,
we have “but” (1,941,535), “although” (104,295), and “even
if” (66,802). After the search, we ended up with 10,187,926
reviews in the remaining set (82% of the original dataset)
and 2,150,438 reviews in the detected set (identified by the
heuristic).
Findings: Game reviews with contrast conjunctions are
indeed more difficult to classify for NLTK, with a median
AUC that is 11% lower than for reviews without contrast
conjunctions. Figure 7 presents the distributions of the AUC
for the sets of reviews without and with contrast conjunctions.
The Wilcoxon rank-sum test shows that the two distributions
are significantly different, with a large (1.0) Cliff’s delta effect
size.
As we can observe, the AUC of reviews without contrast
is much higher (large Cliff’s delta effect size) than the AUC
of reviews with the presence of contrast conjunctions. In fact,
we found a median AUC of almost 0.75 for the group without
contrast, while for the group with contrast the median AUC
is around 0.67 (11% lower).
B. Game Comparison
Detection heuristic: We collected the top-500 most played
games from Steam and, based on this list, we performed a
keyword-based search in our dataset to identify reviews that
mention other games.
We collected the most played games from the SteamDB
platform.6This list was obtained based on the peak number
of players who have played the game. For instance, the
number one game in the list is Playerunknown’s Battlegrounds
(3,257,248 players), followed by Dota 2 (1,295,114 players)
and Counter-Strike: Global Offensive (854,801 players). This
data was collected on January 10th, 2020. Note that, although
the game reviews were collected in 2016, their age does not
impact our analysis.
We performed a keyword-based search on the reviews in
our dataset. We ensured that both the game name being
6https://steamdb.info/
11
TABLE V: Contrast conjunctions and corresponding examples.
Contrast conjunction Example
But Nice Matchmaking, but if you are not premium you have no chance...
Although, though Although I really enjoy this game, I do think that PTTM still remains the best in the series...
Even though, even if Even though it gets progressively difficult and you won’t get the perfect items each run, you’ll
find yourself coming back for more...
Without contrast With contrast
AUC
0 0.64 0.68 0.72 0.76
Fig. 7: AUC distribution for reviews
without and with contrast.
Without comparison With comparison
AUC
0 0.62 0.66 0.7
Fig. 8: AUC distribution for reviews
without and with comparison.
AUC
Baseline
Action
Adventure
Casual
Racing
RPG
Sports
Strategy
0 0.68 0.7 0.72 0.76
Fig. 9: AUC distribution for reviews of
all the game genres and the baseline.
searched and the review text were lower case during the
search. Among the most mentioned games in the reviews,
we found Terraria (31,860), Dota 2 (30,385), and Counter-
Strike (21,418). After the search, we ended up with 11,753,211
reviews in the remaining set (95% of the original dataset) and
585,153 reviews in the detected set (identified by the heuristic).
Findings: Game reviews with comparisons are actually
more difficult to classify for NLTK, with a median AUC
that is 8% lower than for reviews without comparisons.
After training NLTK on both sets, we computed the AUC using
the out-of-sample bootstrap with 1,000 iterations, as we did
previously. Figure 8 presents the distributions of the AUC
for the sets of reviews without and with comparison. The
Wilcoxon rank-sum test shows that the two distributions are
significantly different, with a large (1.0) Cliff’s delta effect
size.
As we can see, reviews without comparison present a higher
AUC than reviews with comparison. In fact, we found a
median AUC of almost 0.71 for the group without comparison,
while for the group with comparison the median AUC is
around 0.65 (8% lower), which indicates that, similarly to the
case of reviews with contrast conjunctions, comparisons can
also degrade the performance of sentiment analysis.
C. Negative Terminology
Detection heuristic: We noticed that some game reviews use
words with a negative connotation, such as kill,evil, and
death. Although such words might refer to negative aspects of
something in a usual context, within the context of games they
might be used without the negative connotation. For instance,
when describing the role of a character in an RPG (Role-
Playing Game) game, one might say they need to defeat
and kill the enemy. Although the review uses (supposedly)
negative words, its final content might be positive towards the
game (i.e., the reviewer might recommend the game even when
using negative words).
For this root cause, instead of adopting the approach as we
did for the previous causes, we propose a stratified training
process for the sentiment analysis classifier based on the game
genre, which we call per-genre training. We used a customized
crawler to collect the game genre from Steam for each review
in our dataset and grouped reviews by genre so we could
train the classifier separately by genre. We found a list of
seven game genres (excluding generic genres reported by
Steam, such as Early Access, Free to Play, and Indie): Action,
Adventure, Strategy, RPG, Casual, Racing, and Sports.
We established a maximum period of one month to collect
the game genres for a randomized version of our data, which
resulted in genres for 4 million reviews. It would be infeasible
to collect the genre for our entire dataset in a timely manner
due to restrictions when using a crawler to collect online data
(such as the limited number of requests allowed per a period
of time). Furthermore, for some cases, the review or the profile
itself was excluded by the user from the Steam platform. In
the case of less popular genres for which we are not able to
sample 100K reviews for the training and testing sets (casual,
racing, and sports genres), we adopted a 80/20 percentage split
to train and test with the bootstrap technique. For instance, if
we had 10K reviews for a specific genre, we would use 8K for
training (80%) and 2K for testing (20%). Table VI presents
the number of reviews for each genre.
Findings: Per-genre training is effective when performing
sentiment analysis on game reviews. Figure 9 presents the
distribution of the AUC for all the genres and also for the
baseline, which is the evaluation of NLTK on the entire dataset
12
TABLE VI: Game genres and corresponding number of re-
views.
Genre Number of reviews
Action 741,569
Adventure 484,236
Strategy 395,595
RPG 372,033
Casual 128,590
Racing 43,899
Sports 33,890
(Section VI). We can see that, for all the genres except for
adventure, the median AUC is higher than the median AUC
for the baseline. In fact, we obtained the following median
AUC values: 0.70 (baseline), 0.71 (action), 0.69 (adventure),
0.71 (casual), 0.74 (racing), 0.72 (RPG), 0.72 (sports), and
0.72 (strategy). The Wilcoxon rank-sum test shows that the
AUC distribution for each genre is significantly different from
the AUC distribution for the baseline with a large effect size.
Reviews that use contrast conjunctions to point out ad-
vantages and disadvantages of the game have the highest
negative impact on the performance (11% lower AUC),
followed by reviews with game comparisons (8% lower
AUC). Furthermore, we show that per-genre training is
effective for sentiment analysis on game reviews as it is
mostly able to improve the performance of NLTK.
IX. RECOMMENDATIONS AND RESEARCH DIRECTIONS
FOR SENTIMENT ANALYSIS ON GAME REVIEWS
In this section, we provide practical recommendations for
performing sentiment analysis on game review data.
No need for huge amounts of data. Through our pre-study we
showed that 100K reviews is a sufficient sample size to train
and test sentiment analysis classifiers on game reviews. We
showed that using more than 100K reviews does not improve
the sentiment analysis performance as it plateaus after 100K
reviews. Note that this is based on a Na¨
ıve Bayes classifier
as we aim to provide recommendations for computationally
accessible approaches rather than computationally intensive
deep learning algorithms. Furthermore, this recommendation
is based on the NLTK configurations adopted in the study
(i.e., the machine learning version of NLTK with the same
preprocessing steps).
Prioritize on studying techniques that can deal with
reviews with advantages and disadvantages of the game.
Based on the impact that each root cause has on the sentiment
analysis performance, we suggest game developers and re-
searchers to develop techniques that can analyze reviews which
use contrast conjunctions to point out the advantages and
disadvantages of the game under review as this might confuse
the classifier. Secondly, we suggest to develop techniques that
can deal with reviews which make comparison to games other
than the game under review or to previous versions of the
game itself. Finally, we suggest the development of techniques
to analyze reviews that contain sarcasm.
Stratify reviews by game genre. Different game genres
have different characteristics in terms of expressions used by
reviewers. Therefore, we recommend to stratify the dataset by
genre and train the classifier separately for each genre. This
approach helps to avoid mixing different types of data when
training the model. For instance, negative words (e.g., evil) are
used for different purposes in reviews of different genres, such
as casual (where the reviewer probably uses it with a negative
connotation) and first-person shooter (where the reviewer does
not intentionally have a negative connotation).
X. CONCLUSION AND FUTURE WOR K
In this paper, we perform a large-scale study to understand
how sentiment analysis works on game reviews. We collected
12 million reviews from the Steam platform. We investigate the
performance of existing sentiment analysis classifiers on game
reviews, identify which factors might impact such performance
and to what extent.
Our study shows that sentiment analysis classifiers do
not perform well on game reviews and we identified root
causes for such performance, such as sarcasm and reviews
with negative terminology. Reviews that point out advantages
and disadvantages of a game (through the use of contrast
conjunctions) have a high negative impact on the performance
(reducing the median AUC by 11%), followed by reviews
that contain comparisons to games other then the game under
review (reducing the median AUC by 8%). Furthermore, we
show that training classifiers on reviews stratified by the genre
is effective and can improve the performance of sentiment
analysis. For all genres except adventure, the median AUC
was higher than the baseline, with significant different AUC
distributions and large effect sizes.
Our study is the first important step towards identifying
what are the root causes for wrong classifications in sentiment
analysis on game reviews and the impact of each cause. Our
study calls upon sentiment analysis and game researchers to
further investigate how the performance of sentiment analysis
on game reviews can be improved, for instance by devel-
oping techniques that can automatically deal with specific
game-related issues of reviews (e.g., reviews with contrast
conjunctions and reviews with game comparisons). Another
future direction is to explore how user characteristics affect the
performance of the sentiment classification of game reviews.
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