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Exploring Language Markers of Mental Health in Psychiatric Stories


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Diagnosing mental disorders is complex due to the genetic, environmental and psychological contributors and the individual risk factors. Language markers for mental disorders can help to diagnose a person. Research thus far on language markers and the associated mental disorders has been done mainly with the Linguistic Inquiry and Word Count (LIWC) program. In order to improve on this research, we employed a range of Natural Language Processing (NLP) techniques using LIWC, spaCy, fastText and RobBERT to analyse Dutch psychiatric interview transcriptions with both rule-based and vector-based approaches. Our primary objective was to predict whether a patient had been diagnosed with a mental disorder, and if so, the specific mental disorder type. Furthermore, the second goal of this research was to find out which words are language markers for which mental disorder. LIWC in combination with the random forest classification algorithm performed best in predicting whether a person had a mental disorder or not (accuracy: 0.952; Cohen’s kappa: 0.889). SpaCy in combination with random forest predicted best which particular mental disorder a patient had been diagnosed with (accuracy: 0.429; Cohen’s kappa: 0.304).
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Citation: Spruit, M.; Verkleij, S.; de
Schepper, K.; Scheepers, F. Exploring
Language Markers of Mental Health
in Psychiatric Stories. Appl. Sci. 2022,
12, 2179.
Academic Editors: Arturo
Montejo-Ráez and Salud María
Received: 21 September 2021
Accepted: 15 February 2022
Published: 19 February 2022
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Exploring Language Markers of Mental Health in
Psychiatric Stories
Marco Spruit 1,2,* , Stephanie Verkleij 3, Kees de Schepper 4and Floortje Scheepers 4
1Leiden University Medical Center (LUMC), Campus The Hague, Leiden University, Turfmarkt 99,
2511 DC The Hague, The Netherlands
2Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1,
2333 CA Leiden, The Netherlands
3Department of Information and Computing Sciences, Utrecht University, Princetonplein 5,
3584 CC Utrecht, The Netherlands;
4University Medical Center Utrecht (UMCU), Utrecht University, Heidelberglaan 100,
3584 CX Utrecht, The Netherlands; (K.d.S.); (F.S.)
Diagnosing mental disorders is complex due to the genetic, environmental and psycholog-
ical contributors and the individual risk factors. Language markers for mental disorders can help
to diagnose a person. Research thus far on language markers and the associated mental disorders
has been done mainly with the Linguistic Inquiry and Word Count (LIWC) program. In order to
improve on this research, we employed a range of Natural Language Processing (NLP) techniques
using LIWC, spaCy, fastText and RobBERT to analyse Dutch psychiatric interview transcriptions
with both rule-based and vector-based approaches. Our primary objective was to predict whether
a patient had been diagnosed with a mental disorder, and if so, the specific mental disorder type.
Furthermore, the second goal of this research was to find out which words are language markers
for which mental disorder. LIWC in combination with the random forest classification algorithm
performed best in predicting whether a person had a mental disorder or not (accuracy: 0.952; Cohen’s
kappa: 0.889). SpaCy in combination with random forest predicted best which particular mental
disorder a patient had been diagnosed with (accuracy: 0.429; Cohen’s kappa: 0.304).
Keywords: language marker; mental disorder; deep learning; LIWC; spaCy; RobBERT; fastText; LIME
1. Introduction
Mental disorders make up a major portion of the global burden of disease [
], and in
2017, 10.7% of the global population reported having or having had a mental disorder [
This prevalence is not staying stead, but is rising mainly in developing countries [
]. Fur-
thermore, mental disorders have a substantial long term impact on individuals, caregivers
and society [
]. The challenge of diagnosing a mental disorder is the complexity of multiple
genetic, environmental and psychological contributors and individual risk factors [4].
Research has shown that people with mental health difficulties use distinctive language
patterns [
]. Until now, the Language Inquiry and Word Count (LIWC) toolkit has been
the main focus for identifying language markers [
]. This toolkit of Natural Language
Processing (NLP) techniques calculates the number of words of certain categories that are
used in a text based on a dictionary [
]. LIWC is a traditional programme in the sense that it
analyses texts with symbolic (i.e., deterministic and rule-based) techniques, predominantly
at the word level. LIWC does not use subsymbolic (i.e., probabilistic and vector-based)
NLP techniques such as word vector representations within neural networks.
The objective of our research was to compare the performance of LIWC with the
performances of other NLP techniques in the quest to provide useful insights into Dutch
psychiatric stories. In this paper, we compare the performances of LIWC [
], spaCy [
Appl. Sci. 2022,12, 2179.
Appl. Sci. 2022,12, 2179 2 of 17
fastText [
] and RobBERT [
] when applied to psychiatric interview transcriptions. SpaCy
provides, among other things, a dependency grammar parser to syntactically process texts.
This NLP technique can provide insights by unravelling the grammatical structure of each
sentence, and it will provide information about the grammatical relationships between
words [
]. By using this technique, we aimed to uncover the different uses of grammar
by patients with different mental illnesses. This provides further insights into the stylistic
differences between people with and without mental disorders. fastText and RobBERT
were selected because both techniques employ deep learning models. Deep learning
exploits layers of non-linear information processing for both supervised and unsupervised
tasks [
]. We hypothesise that deep learning techniques can provide more insights than
other methods into these complex mental health disorders.
2. Related Work
This research is not the first to attempt to identify language markers associated with
mental disorders. Several researchers already compared mental disorders using the LIWC
tool [
]. We introduce and compare several state-of-the-art alternative NLP approaches
to identifying language markers’ associations with mental health disorders.
2.1. Language Markers for Mental Health Disorders
A literature study was performed to review earlier work related to language mark-
ers for mental health disorders. The snowballing method was used to find the relevant
literature. Both backward snowballing and forward snowballing were employed [
A curated set of recent papers on language markers in mental healthcare was used as
the starting point [
]. Then, one or two levels deep were snowballed back and
forth. The number of levels snowballed depended on whether new relevant literature was
found. Whenever a dead end was reached, the snowballing procedure was stopped. We
selected Google Scholar (with a proxy from Utrecht University) to execute the following
search queries:
“Language marker” “mental health” “LIWC”
“Language marker” “mental health” “language use”
“Mental health” “deep learning”
“Dutch” “parser” “NLP”
“BERT” "mental health” “classification”
“Alpino” “dependency parser”
“spaCy” “lemma” “dependency parser”
“Language” in conjunction with the words below:
Bipolar Disorder
Borderline personality disorder
Eating disorder
Generalised anxiety disorder
Major depressive disorder
Table 1summarises our findings related to ten different mental disorders, highlighting
their uses of language. These include mainly characteristic use of pronouns (Pron), the
degree ([n]ormal/[i]mpaired) of semantic coherence (SC) and usage of topical words. We
only list the disorders that appear in our dataset as the main diagnosis; the N column
shows the number of patients.
We found that people with attention deficit hyperactivity disorder (ADHD) use more
third-person plural (3pl) pronouns, less words of relativity [
] and more sentences, but less
clauses per sentence [
] than normal. Autism is strongly linked to motion, home, religion
Appl. Sci. 2022,12, 2179 3 of 17
and death features [
]. Furthermore, people with autism are more self-focused, because
they use more first-person singular (1sg) pronouns [
]. People who are bipolar are also
more self-focused and use more words related to death [
]. The use of more swear words,
words related to death and third-person singular (3sg) pronouns, and less use of cognitive
emotive words are associated with borderline personality disorder (BPD) [
]. Eating
disorders, consisting of bulimia, anorexia and eating disorders not otherwise specified,
are associated with the use of the words related to the body, negative emotive words, self-
focused words and cognitive process words [
]. People with generalised anxiety disorder
(GAD) produce more sentences which lack semantic coherence [
]. Furthermore, they
use more tentative words and impersonal pronouns, and they use more words related to
death and health [
]. Major depressive disorder (MDD) has a strong appearance of being
more self-focused, involving more past tense and repetitive words and producing short,
detached and arid sentences [
]. Obsessive compulsive disorder (OCD) is associated with
words related to anxiety and cognitive words. Researchers do not yet agree on the language
cues associated with post-traumatic stress disorder (PTSD). One study showed that there
were no cues [
], yet another study showed that people with PTSD use more singular
pronouns and words related to death and less cognitive words [
]. Finally, research shows
that a lack of semantic cohesion [
], usage of words related to religion and hearing voices
and sounds are associated with schizophrenia [5]. Further details are available in [24].
Table 1. Overview of associated language markers for ten mental health disorders.
Disorder Pron SC Word Use More N
ADHD 3pl - - Relativity, more sentences, less clauses 4
Autism 1sg - Motion, home, religion and death - 5
Bipolar 1sg - Death - 7
BPD 3sg n Death Swearing, less cognitive emotion words 5
Eating 1sg - Body Negative emotion words 10
GAD imprs i Death and health Tentative words 4
MDD 1sg i - Inverse word-order and repetitions 11
OCD 1sg - Anxiety More cognitive words 4
PTSD sg - Death Less cognitive words 6
Schizophrenia 3pl i Religion Hearing voices and sounds 16
2.2. NLP Techniques for Identifying Language Markers
We investigated the following four basic approaches in NLP for identification of
language markers: lexical processing from a lexical semantics perspective, dependency
parsing from a compositional semantics viewpoint, shallow neural networks in a stochastic
paradigm and deep neural networks employing a transformer-based architecture.
2.2.1. Lexical Processing
Research so far on exploring language markers in mental health has been done mainly
with Linguistic Inquiry and Word Count (LIWC) [
]. LIWC is a computerised text-analysis
tool and has two central features: a processing component and dictionaries [
]. The
processing feature is the program which analyses text files and goes through them word by
word. Each word is compared with the dictionaries and then put in the right categories.
For example, the word “had” can be put in the categories verbs, auxiliary verbs and past
tense verbs. Next, the program calculates the percentage for each category in the text; for
example, 17% of the words may be verbs. A disadvantage of the LIWC program is that
it ignores context, idioms, sarcasm and irony. Furthermore, the 89 different categories
are based on language research. However, this does not guarantee that these categories
represent reality, because categories could be missing.
Appl. Sci. 2022,12, 2179 4 of 17
2.2.2. Dependency Parsing
The syntactic processing of texts is called dependency parsing [
]. This processing
is valuable because it forms transparent lexicalised representations and it is robust [
Furthermore, it also gives insights into the compositional semantics, i.e., the meanings of
a sentence’s individual words or phrases [
]. Small changes in the syntactic structure
of a sentence can change the whole meaning of the sentence. For example, John hit Mary
and Mary hit John contain the same words, but have different meanings. It is said that
compositionality is linked to our ability to interpret and produce new remarks, because
once one has mastered the syntax of a language, its lexical meanings and its modes of
composition, one can interpret new combinations of words [
]. Compositionality is the
semantic relationship combined with a syntactic structure [
]. Compositional semantics is
driven by syntactic dependencies, and each dependency forms, from the contextualised
sense of the two related lemmas, two new compositional vectors [
]. Therefore, the tech-
nique required for extracting the compositional semantics needs to contain a dependency
parser and a lemmatizer.
Choi et al. [25]
compared the ten leading dependency parsers
based on the speed/accuracy trade-off. Although Mate [
], RBG [
] and ClearNLP [
perform best in unlabeled attachment score (UAS), none of them includes a Dutch dic-
tionary, which was needed for this research. However, spaCy does include a Dutch
dictionary. Other Dutch dependency parsers are Frog [
] and Alpino [
]. Both Frog
(, accessed on 17 October 2021)
and spaCy (, accessed on 17 October 2021) include the Dutch
dictionary corpus of Alpino, but due to equipment constraints, we selected spaCy for the
dependency parsing task.
2.2.3. Shallow Neural Networks
Features made for traditional NLP systems are frequently handcrafted, time consum-
ing and incomplete [
]. Neural networks, however, can automatically learn multilevel
features and give better results based on dense vector representations [
]. The trend
toward neural networks has been caused by the success of deep learning applications and
the concept of word embeddings [
]. Word embeddings, such as the skip-gram model and
the continuous bag-of-words (CBOW) model [
], distribute high-quality vector representa-
tions and are often used in deep learning models as the first data processing layer [
]. The
word2vec algorithm uses neural networks to learn vector representations [
]. It can use the
skip-gram model or the CBOW model, and it works for both small and large datasets [
However, out-of-vocabulary (OOV) words, also referred to as unknown words, are a com-
mon issue for languages with large vocabularies [
]. The fastText model overcomes this
problem by handling each word as a bag-of-character n-gram. This is achieved by using the
skip-gram model from word2vec as an extension. These n-grams are used to represent the
sums of the n-gram vectors [
]. Finally, it is worth noting that both Word2vec and fastText
are said to employ a shallow neural network architecture; i.e., their neural networks only
define one hidden layer, which explains why these models are known to be many orders
of magnitude faster in training and evaluation than other deep learning classifiers, while
often performing as well as those classifiers in terms of accuracy [38].
2.2.4. Deep Neural Networks
In 2017 the transformer neural network architecture was introduced [
], which
much improved NLP tasks such as text classification and language understanding [
Bidirectional encoder representations from transformers (BERT) is an immensely popular
transformer-based language representation model designed to pretrain, from unlabelled
text, deep bidirectional representations [
]. The multilingual version of BERT is simply
called mBERT. A more recent and improved version of BERT is RoBERTa, which stands
for robustly optimised BERT approach [
]. The main changes are that RoBERTa trains for
longer, on more data, with bigger batches and on longer sequences [42].
Appl. Sci. 2022,12, 2179 5 of 17
2.2.5. Neural Networks for Dutch
In Table 2an overview of the different neural networks can be seen. The choice of
best fit is limited, because of the small and Dutch dataset. Two neural networks were
chosen for this research, one based on words and one based on sentences. Furthermore, the
neural networks had to have a Dutch model. Thus, the choice was between word2vec and
fastText at the word-level and between BERT, mBERT and RoBERTa at the sentence level.
Other models, such as ClinicalBERT, could also be used in combination with a transfer
learning model such as the Cross-lingual Language Model (XLM) to tackle the Dutch data.
However, these models have not yet been used extensively in the medical domain [
]. This
could be because the interpretability and performance of a model are equally important
in the medical domain. Even though deep learning models can perform better than the
more traditional models, they are hard to explain or understand [
]. Hence, this approach
was not used for this research. Furthermore, fastText has proven that it results in better
performance in comparison to Word2vec [
] and it is able to handle OOV words as well,
because of the n-grams.
Table 2.
Overview of neural network models under consideration for identifying language markers
in Dutch.
Model Dutch Architecture Input Level Selected
Word2Vec Yes CBOW & Skip-gram Word No
fastText Yes RNN Word Yes
ELMo Yes (Bi)LSTM Sentence No
ULMFit Yes Transformer Sentence No
GPT No Transformer Sentence No
GPT-2 No Transformer Sentence No
GPT-3 No Transformer Sentence No
BERT Yes Transformer Sentence No
RoBERTa/RobBERT Yes Transformer Sentence Yes
ClinicalBERT No Transformer Sentence No
XLnet No Transformer-XL Sentence No
StructBERT No Transformer Sentence No
ALBERT No Transformer Sentence No
T5 No Transformer Sentence No
The Dutch version of BERT is called BERTje [
], the Dutch version of RoBERTa is
called RobBERT [
] and mBERT is the multilingual BERT with support for more than
100 languages, including Dutch [
]. A choice between the three BERTs was made by look-
ing at their performances with respect to the classification task, because that was the focus
of this research. The research of Delobelle et al.
shows that RobBERT (ACC = 95.1%)
performs best on classification tasks compared to mBERT (ACC = 84.0%) and BERTje
(ACC = 93.0%) with a full dataset. Therefore, the neural networks selected for this research
were fastText and RobBERT.
3. Methodology
3.1. Dataset and Preprocessing
The dataset used for this research was obtained from the Verhalenbank (“Storybank”)
of the University Medical Centre Utrecht (UMCU) in The Netherlands. Its psychiatry
department has been collecting stories about mental illness of people who have or had
psychiatric issues or were in contact with people with psychiatric issues. Interviews were
conducted with
, caregivers and medical employees to gain new leads which
could benefit the recovery of patients. The interviews were then transcribed into anonymous
stories and put on the website of the Verhalenbank (,
accessed on 17 October 2021). The dataset consists of 108 interviews with 11 diagnostic
Appl. Sci. 2022,12, 2179 6 of 17
labels; 36 are without mental disorder labels. The diagnoses were assigned by multiple
doctors and based on other material than the interviews. The interviews were all between
60 and 90 min long, and the corresponding transcripts are between 6782 and 9531 words in
length. The split used for this research was 80% training and 20% testing. There were not
enough data to have a validation set. Source code for the data analysis is available at: https:
//, accessed on 17 October 2021.
3.2. Data Analysis
This exploratory study compares the classification performances of different NLP
techniques and looks at which language cues could predict if a person has a mental
disorder, and if so, which kind of mental disorder. The four different techniques were
applied to the two tests. The first test consisted of deciding between mental disorder and
no mental disorder; and the second one consisted of deciding between the different mental
disorders. After applying the techniques, predictions were made. For LIWC and spaCy, the
classification algorithms decision tree, random forest and support vector machine (SVM)
were used by means of the default configurations of the R packages rpart,randomForest
and e1071, respectively. The deep learning techniques used their default prediction models
without incorporating a transfer learning step [
]. Next, the techniques and predictions
were applied again after removing the stop words, as listed in the Dutch portion of the
NLTK Python package [
], after which the interviews and the predictions were compared.
Furthermore, to gain further insight into the predictions of fastText and RobBERT, LIME
(Local Interpretable Model Agnostic Explanation) was applied [49].
4. Results
4.1. Descriptive Statistics
An overview of the number of people per mental disorder in our dataset is shown
in Figure 1. The group with dissociation (a disconnection between a person’s memories,
feelings, perceptions and sense of self) contains the least number of people in this dataset;
the group with psychosis is the largest. Furthermore, there are two labels about personality.
Personality includes obsessive-compulsive personality disorder, avoidant personality dis-
order, dependent personality disorder and unspecified personality disorders. Personality+
in this research only includes borderline personality disorder (BPD). Figure 2shows a
boxplot of the number of words per mental disorder, which indicates that people with
eating disorders use less words than people without eating disorders.
Figure 1. Columnchart of number of people per mental disorder in the dataset.
Appl. Sci. 2022,12, 2179 7 of 17
4.2. Predictions
Table 3shows the accuracies in the two tests and Cohen’s Kappa per prediction. The
best performing classifiers are highlighted in bold text. The LIWC program in combination
with the random forest algorithm achieved the highest accuracy when comparing mental
disorder to no mental disorder (accuracy: 0.952). SpaCy reached the highest accuracy when
comparing the different kinds of mental disorder (accuracy: 0.429).
Figure 2. Boxplot of number of words per mental disorder in the dataset.
Cohen’s kappa was used to assess the inter-classifier agreement [
]. This metric takes
the probability that the 10 different labels (in this case) agree by chance into consideration
when quantifying how much they agree. Cohen’s kappa was calculated for each model
and prediction algorithm. If the coefficient is below 0.4, there is a slight correlation between
the models (and with a negative kappa it is even below chance level). A kappa of above
0.6 means that the classifiers have a substantial agreement; for example, see the LIWC-
output with the SVM model in the MD (mental disorder) vs. control group comparison.
When the kappa is between 0.8 and 1.0, this indicates that the classifiers have almost
perfect agreement. This applies to the LIWC-output with the random forest model in the
second comparison with a kappa of 0.889. Care should be taken when interpreting Cohen’s
kappa [
], but the fact that the item with the highest kappa also has the highest accuracy
is reassuring. The low accuracy of the second comparison can be explained due to a dataset
having only 72 interviews from people with mental disorders and 10 different kinds of
mental disorders.
What also can be seen in Table 3in the sixth and seventh columns is that without stop
words spaCy performed less accurately, while LIWC, fastText and RobBERT performed
almost the same in both comparisons.
Appl. Sci. 2022,12, 2179 8 of 17
Table 3. Accuracy and Cohen’s Kappa for the model predictions (with and without stop words).
Comparison Input Model Accuracy Kappa Accuracy No Stopwords Kappa No Stopwords
LIWC-output decision tree 0.857 0.667 0.857 0.674
LIWC-output random-Forest 0.952 0.889 0.952 0.877
LIWC-output SVM 0.857 0.64 0.905 0.738
spaCy decision tree 0.810 0.391 0.444 0.309
spaCy random-Forest 0.762 0.173 0.389 0.370
spaCy SVM 0.714 0.115 0.528 0.275
raw data fastText 0.643 0.172 0.607 0.072
raw data RobBERT 0.607 0.000 0.607 0.000
LIWC-output decision tree 0.286 0.157 0.286 0.177
LIWC-output random-Forest 0.214 0.120 0.214 0.144
LIWC-output SVM 0.286 0.114 0.143 0.0718
spaCy decision tree 0.143 0.0120 0.071 0.052
spaCy random-Forest 0.429 0.304 0.214 0.078
spaCy SVM 0.357 0.067 0.143 0.091
raw data fastText 0.286 0.000 0.200 0.000
raw data RobBERT 0.200 0.000 0.267 0.120
4.3. Interpretation
In this section, we elaborate on our findings regarding the performances of the LIWC,
SpaCy, fastText and RobBERT approaches to NLP for language marker identification.
4.3.1. Lexical Processing with LIWC
Figure 3shows the decision tree for the LIWC-output. If an interview transcription
consisted of more than 5.4% of the first-person singular pronoun, than it was classified as
being of a person with a mental disorder. If not and if less than 8.5% of the words were
related to social concepts, then the interview was classified as being of a person with no
mental disorder. Furthermore, the decision tree categories of the LIWC tool were visualised
in a stripchart (jitter) plot, a fragment of which is shown in Figure 4. In particular, this plot
effectively illustrates the potential to identify people with and without a mental disorder
based on the empirical frequencies of hypothesised LIWC category occurrences, such as
first-person singular pronoun (1sg), further strengthening the rationale behind this feature
being the root decision of the LIWC decision tree shown in Figure 3.
Furthermore, we investigated the LIWC’s feature importance using a random forest
classifier to determine which variables added the most value to our binary predictions.
Figure 5shows the top 10 variables that impacted the classification.
4.3.2. Dependency Parsing with SpaCy
Similarly, we investigated the SpaCy feature importance using a random forest classi-
fier to determine which n-grams added the most value to our binary predictions.
Figure 6
shows the top 10 variables that impact the classification. In addition, we present the mean,
standard deviation (sd) and standard error (se) for each n-gram in Figure 7. A Mann–
Whitney U test revealed no significant difference between people with and without mental
disorders in their usage of the following four spaCy variables: denken_denken_ROOT,
gaan_gaan_ROOT, ja_ja_ROOT and zijn_zijn_ROOT. Finally, we provide example sentences
for each of the identified SpaCy language markers in Table 4.
Appl. Sci. 2022,12, 2179 9 of 17
Figure 3.
Example decision tree with two LIWC parameters (parameter imeans the percentage of
first-person pronouns and parameter social the percentage of words referring to others, such as they;
each box lists the choice between mental disorder or not, the chance of the class being no mental
disorder and the percentage of the data that fall in this box).
Figure 4.
This stripchart plot illustrates the potential to identify people with and without a mental
disorders based on the empirical frequencies of hypothesised LIWC category occurrences, e.g.,
first-person singular pronoun (1sg).
Appl. Sci. 2022,12, 2179 10 of 17
Figure 5. Top 10 LIWC features by importance in binary classification.
Figure 6. Top 10 SpaCy features by importance in binary classification.
4.3.3. Neural Networks with fastText and RobBERT
LIME was applied to both fastText and RobBERT to gain further insight into the
black-box neural network models. LIME is a well-known and well-understood surrogate
model-based approach to help explain model predictions by learning surrogate models
using an operation called input perturbation [
]. For each sentence, subsamples of words
were generated and fed to the model, so for each word the predictions for subsamples
with and without this word could be compared, and subsequently the contribution of this
word could be assessed. For example, quote 1 was from someone who had been diagnosed
with schizophrenia, and the text was labelled by RobBERT as mental disorder. The word
“eh” has been highlighted because it explains according to LIME why it was labelled as
mental disorder (class = 0). Note that the original quote is in Dutch, but for convenience we
Appl. Sci. 2022,12, 2179 11 of 17
provide English translations here. In addition, “[silence]” means a pause that was judged
as meaningful by the transcriber of the interview. In Figure 8, the ten words with the
highest usage can be seen. Some words appear multiple times in the figure. This is because
LIME looks locally at a text and every word appears in a different context. This also means
that sometimes a word will be an explanation for a mental disorder and other times not,
especially for context sensitive algorithms like RobBERT.
Figure 7. Top 10 SpaCy n-gram features in binary classification.
Table 4. Example sentences containing the top 6 spaCy variables.
spaCy Variable Example Sentence
ik_doen_nsubj Ik doe normaal, haal mijn studie en gebruik geen drugs en ben niet irritant
I_do_nsubj ‘I do normal, get my degree and do not use drugs and am not irritating’
ik_gaan_nsubj ik ben meer waard dan dit, ik ga voor mezelf opkomen.
I_go_nsubj ‘I am worth more than this, I’m going to stand up for myself’
ik_hebben_nsubj Ik heb ook behandelingen gehad, of een behandeling gehad
I_have_nsubj ‘I have also gotten treatments, or got a treatment’
ik_komen_nsubj Ja, ik kwam in de bijstand
I_come_nsubj ‘Yes, I came into welfare’
er_zijn_advmod Er zijn zo veel vrouwelijke sociotherapeuten in heel [naam][centrum] die opgeroepen kunnen worden
there_are_advmod ‘There are so many female sociotherapists in [name][centre] who can be called’
ze_hebben_nsubj Al een tijdje maar ze hebben nooit wat aan mij verteld
they_have_nsubj ‘For some time, but they have never told me anything’
Appl. Sci. 2022,12, 2179 12 of 17
Figure 8.
LIME explanation for quote 1 (top 10 words and how much they approximately contribute
to the classification decision).
Figure 9.
LIME explanation for quote 2 (top 10 words and how much they approximately contribute
to the classification decision).
Quote 1: “I ehm, [silence] the most poignant I will you. Yes, the most poignant
what I can tell you is that, I have weekend leave on the weekend and then
[name_of_wife] and I lay together in bed. Furthermore, nothing happens there.
As I do not need that, haha. However, I cannot even feel that I love her. I know it,
that I love her. Furthermore, I know that my wife is and I, and I. However, that is
all in here eh, but I do not feel it. Furthermore, that is the biggest measure which
you can set· · · Yes. Furthermore, I talked about it with her.”
Quote 2 is from someone with an eating disorder and was analysed with fastText. The
word “eh” was highlighted because it explained why the transcription was labelled as
Appl. Sci. 2022,12, 2179 13 of 17
coming from a patient with a mental disorder (class = _label_md). Figure 9shows the ten
words with the highest probabilities from that transcription.
Quote 2: “Yes it gives kind of a kick or something to go against it and to see that
people you really eh yes I don’t know. That your that your eating disorder is
strong and people find that then. Then, you think oh I am good at something.
Then, yes I don’t know. Then you want there that you want to be doing something
you are good at
· · ·
Eh I am able to walk again since two months. Before I eh
stayed in bed and in a wheelchair around half a year, because I eh could not walk
myself. Furthermore, I was just to weak to do it. and eh yes I still cannot do quite
a lot of things. I am really happy that I can walk again by myself.”
Other text also heavily featured conversational words such as “eh,” “well,” and “yes”
in the LIME analyses. This suggests that perhaps for these interviews the difference between
mental disorder and no disorder was more prevalent in the manner of speaking than in the
topics they addressed.
Table 5shows samples of eight interviews whose words resulted in the assignment
of the mental disorder (MD) label or the no mental disorder (noMD) label. The first four
interviews were analysed with stop words, and as can be seen, most of the words are stop
words or “generally not meaningful” words. They could, however, be related to insightful
words, which are also showen in the quotes. This could be supposedly because RobBERT
looks both left and right in the context of a word in all layers of the transcription and then
conditions it. Apparently, some words appear both in the mental disorder column and in
the no mental disorder column, simply because these words appear in different contexts.
Such words can contribute to a mental disorder classification in some language contexts,
whereas in another context they do not. To further investigate, we removed all stop words
from the last four interviews to determine whether LIME found more meaningful words.
For example, in interview 7 with the fastText model, LIME found the words “psychiatrics”
and “performance” as markers for a mental disorder, whereas in interview 8 LIME found
the words “healing” and “job”. In conclusion, without stop words we tended to find
moderately more insightful words than with stop words. However, the words found
by LIME are different for almost every interview and thus not yet applicable to support
analyses of other interviews.
Table 5. LIME output of fastText and RobBERT for a sample of eight interviews.
ID MD SW RobBERT fastText Words MD BERT Words noMD BERT Words MD fastText Words noMD fastText
1 Y Y 0.68 0.77
everyone, too, because,
Yes, For example,
too , Yes, I, did
-yes, with, is,
· · · , common, me
from, common,
common, eh
2 Y Y 0.55 0.69 feel, allowed, I, really,
eh, angry, they, You [name], there together, am, well, well. am, I, me, my
3 N Y 0.39 0.45
happy, the, looking back,
Well, belongs, eh,
always, no, well, think
-say, come, yes,
and, causing
not, that, [place name],
week, say
4 N Y 0.37 0.23 could, can, Furthermore, That,
sat, be, chats, and, whole walked protected, to, is, do, bad,
have, is, physical, am walks
5 Y N 0.68 0.77 ehm, one, bill, yes,
distraction, recovery sat, eh, real, goes yes, well, that,
yes, well, rest if, but, better, care
6 Y N 0.58 0.65 eh
hospital, Furthermore, whole,
whole, she, one,
also, eh, again
whole, completely,
· · · , further, times
stood, sick,
selfish, and, ehm
7 N N 0.41 0.46
eh, nineteen ninety
seven, of, notices of
objection, say, team
car, ehm, team,
through, However,
one, he
that, en route,
exciting, we, go, and
8 N N 0.49 0.43 married, common, a, sit,
heaven, times, and, The ehm, ehm sewn, healing,
and, but, job
huh, hear, term,
ready, busy
Appl. Sci. 2022,12, 2179 14 of 17
4.4. Summary of Findings: Language Markers
Table 6shows an overview of the uncovered language markers for LIWC and spaCy.
The 1SG LIWC pronoun notably came out as a language marker for a person with a mental
disorder. In spaCy, 1SG was also the basis for labelling a mental disorder. The
W; p< 0.05
caption of the rightmost column refers to the Mann–Whitney two-tailed U tests that were
performed to determine whether the means of the two groups per variable were equal to
each other.
Unfortunately, we did not uncover clear patterns in the LIME results of the Rob-
BERT and fastText neural network-based models, as different words were found for every
interview to indicate either a mental disorder or no mental disorder.
Table 6. Summary of language markers uncovered by LIWC and spaCy.
Language Marker Mental Disorder W; p< 0.05
1sg Yes 2487
focuspast Yes 1856
affiliation No 380
drives No 568
female No 937
male No 767
3sg No 454
social No 281
3pl No 882
1pl No 217.5
ik_doen_nsubj Yes 1700.5
ik_gaan_nsubj Yes 1726
ik_hebben_nsubj Yes 1796.5
ik_komen_nsubj Yes 1852.5
er_zijn_advmod No 849
ze_hebben_nsubj No 768.5
4.5. Focus Group
Furthermore, the results of the different models were discussed in a qualitative focus
group session with UMCU data scientists, researchers and psychologists to better under-
stand the outcomes. We discussed three key observations. First, the data scientists noted
that the data used for this research are static data—i.e., somebody told their story and that
was it. No new data from this particular person were added at a later time. The group
hypothesised that following a person in their healing process, including their language
usage, over a longer period of time, would result in additional relevant datapoints, and
therefore could reveal additional interesting outcomes.
Second, the language markers found by LIWC and spaCy were discussed. The data
originated from both people with mental disorders who told their own personal stories
and from medical employees and family members who talked about people with mental
disorders. This dual data origin situation likely influenced the outcome of this research.
When an individual tells his own personal story, he will probably use more 1sg pronouns.
Furthermore, when a health professional discusses an experience with a patient, he will
likely use more 3sg and 3pl pronouns. Finally, people with mental disorders also shared
their personal stories when they were not in an acute phase, and then, they could talk more
about a completed story in their past. Therefore, the uncovered language markers actually
make a lot of sense, according to the experts.
Third, rigid classifications are being abandoned in psychiatry, because they do not
really help a person, according to some psychologists. However, if the current outcome
classification will be changed depending on how far someone is in their healing process,
one could find additional interesting results. The models discussed in this research could be
applied for this new direction. To exemplify this, it was hypothesised that a person who is
Appl. Sci. 2022,12, 2179 15 of 17
further into his healing process will tell a more integrated story about his past than a person
who is less far. In other words, “focuspast” could be a marker for someone being further
into the healing process. Another proposition was that this research could be used to look
at symptoms instead of being used for diagnostic assistance: what kind of treatment will
help a person based on how he speaks? Another idea is to look at suicidality or aggression:
what can a text tell us about that? Put differently, find out what a person is not explicitly
saying, by analysing the deeper layers to find possible patterns or symptoms. One domain
expert concluded: “The strength of this research lays not in the exact results, but in the
application of the different models and the potential questions which could be answered
by these models.”
5. Discussions and Conclusions
We have explored language markers in Dutch psychiatric interview transcriptions.
We particularly focused on comparing the performances of traditional machine learning
algorithms trained on LIWC and spaCy inputs with neural network approaches such as
fastText and RobBERT, in predicting mental disorders. We found that the best performing
technique in terms of determining whether a person has a mental disorder based on their
word choices was LIWC in combination with random forest as the classification algorithm,
which reached an accuracy of 0.952 and a Cohen’s kappa of 0.889. Our hypothesis that the
neural network approaches of fastText and RobBERT would perform best was not borne
out. Several reasons may be posited. First, the pretrained language models of fastText and
RobBERT did not for the most part consist of (transcribed) interview data. Second, the
dataset was rather small (108 interviews) and the concept under consideration (mental
illness) is not immediately apparent from a text. This suggests that for similar tasks with
small datasets it may be best to use a dedicated algorithm such as LIWC, as it uses only a
small selection of curated variables.
With regard to differentiating between mental illnesses, spaCy in combination with
random forest predicted best which mental disorder each person had with an accuracy-
score of 0.429 and a Cohen’s kappa of 0.304. This moderate accuracy score can be explained
due to the fact that the dataset of people with mental disorders only included 72 interview
transcriptions and yet 10 mental disorder labels.
Finally, stop words did not appear to have that much influence on the performance of
the classifiers except when employed using spaCy. We presume that is ws due to spaCy
analysing the text from a grammatical point of view. When stop words are missing, spaCy
cannot deduce the correct syntactic dependencies. Further work will focus on exploring
additional model explainability techniques with differing explainability mechanisms and
visualisation techniques in comparison to LIME, and investigating alternative NLP models
in combination with an expanded data collection.
Ultimately, we argue that better understanding of a person’s language use through
the identification of language markers will result in better diagnosis of that person’s men-
tal health state, similar to the identification of a person’s biomarkers. The impressive
recent advancements within the field of Natural Language Processing are now allow-
ing us to recalibrate our ambitions regarding language marker identification in informal
patient narratives.
Author Contributions:
Conceptualization, M.S. and F.S.; Data curation, K.d.S.; Formal analysis, S.V.;
Funding acquisition, M.S.; Investigation, S.V., M.S. and K.d.S.; Methodology, M.S. and S.V.; Project
administration, S.V.; Resources, M.S., K.d.S. and F.S.; Software, S.V.; Supervision, M.S., K.d.S. and
F.S.; Validation, F.S.; Writing—original draft, S.V.; Writing—review and editing, M.S. All authors have
read and agreed to the published version of the manuscript.
This research was supported by the Computing Visits Data (COVIDA) research programme
of the Strategic Alliance Fund of Utrecht University, University Medical Center Utrecht and Technical
University Eindhoven (round 2019).
Appl. Sci. 2022,12, 2179 16 of 17
Institutional Review Board Statement:
The UMC Utrecht Medical Research Ethics Committee on
5 October 2016 confirmed with reference number WAG/mb/16/030724 that the Medical Research
Involving Human Subjects Act (WMO) does not apply in the context of the Psychiatrieverhalenbank
project with reference 16/626.
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
Verhalenbank study.
Data Availability Statement:
See Section 3.1 for more information on the Verhalenbank (“Storybank”)
dataset which is available at, accessed on 17 October 2021.
Conflicts of Interest: The authors declare no conflict of interest.
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