Identifying Depression Clues using Emotions and AI

Ricardo Martins

, Jos

e Jo

ao Almeida

, Pedro Henriques

and Paulo Novais

Department of Informatics, Algoritmi Centre, University of Minho, Braga, Portugal

Keywords:

Sentiment Analysis, Natural Language Processing, Machine Learning.

Abstract:

According to the World Health Organization (WHO), close to 300 million people of all ages suffer from de-

pression. Also, for WHO, depression is the leading reason for disability worldwide and is a major contributor

to the global burden of disease. Different than the mood ﬂuctuation raised by the common life’s activities,

depression can be a serious health problem, particularly when it is a long-term and mid/high intensity. Luck-

ily, despite depression is a silent disease, people when suffering leaves some clues. Due to the massive use

of social media, these clues can be collected through the texts posted on social media, such as Twitter, Face-

book, Instagram, and later, analysed to identify if the writing style matches with a depressive pattern. This

paper presents an approach that can be applied by Machine Learning models to help psychologists to identify

depressive clues in texts. The model examines proﬁles on Twitter based on clues provided by users in their

posts. Combining Sentiment Analysis, Machine Learning and Natural Language Processing techniques, we

achieved a precision of 98% by Machine Learning models when identifying Twitter proﬁles that post potential

depressive texts.

1 INTRODUCTION

In our social relationships, the chance of knowing

someone who is suffering or suffered from depres-

sive disorders is high. According to United Nations

(UN), the worldwide population is about 7.6 billion

habitants, when compared to 300 million

of depres-

sive people according to WHO (Organization et al.,

2017), draws attention to the fact that every 25 people

in the world, 1 suffers from depression. Yet according

to WHO, “depressive disorders are characterized by

sadness, loss of interest or pleasure, feelings of guilt

or low self-worth, disturbed sleep or appetite, feelings

of tiredness, and poor concentration. Depression can

be longlasting or recurrent, substantially impairing an

individual’s ability to function at work or school or

cope with daily life. At its most severe, depression

can lead to suicide.”

Having the words of Schafer (Schafer, 2011) in

https://orcid.org/0000-0003-1993-5343

https://orcid.org/0000-0002-0722-2031

https://orcid.org/0000-0002-3208-0207

https://orcid.org/0000-0002-3549-0754

https://news.un.org/pt/story/2017/06/1589091-populacao-

mundial-atingiu-76-bilhoes-de-habitantes

https://www.who.int/news-room/fact-sheets/detail/

depression

mind (“if the eyes are the window to the soul, then

words are the gateway to the mind”), the symptoms

associated with depressive disorders are evident in

texts produced by depressive people. So, Twitter,

Facebook and web forums are an excellent source of

information to collect clues about the messages au-

thors’ mental wellness. Automated analysis of social

media can provide detection methods, and, once iden-

tiﬁed an individual as depressive, an assessment, sup-

port and treatment can be provided sooner.

In this paper, we present an approach based on

Machine Learning, Sentiment Analysis and Natural

Language Processing to identify depressive proﬁles

on Twitter, based on the messages posted. It is not

our intention to identify different levels of depres-

sion which authors can have. To reach this objective,

we adopted the emotion model deﬁned by Plutchik

(Plutchik, 1984), because we consider more realis-

tic due to it differentiate the emotions in more cat-

egories than Ekman (Ekman, 1992), despite easy to

use and able to represent different emotions through

dyads emotion.

The remainder of this paper is as follows: Section

2 presents some recent research works on depression

identiﬁcation and points out the most relevant differ-

ences holding among them and our proposal. Sec-

tion 3 introduces the concept of emotional proﬁle, and

Martins, R., Almeida, J., Henriques, P. and Novais, P.

Identifying Depression Clues using Emotions and AI.

DOI: 10.5220/0010332811371143

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 2, pages 1137-1143

ISBN: 978-989-758-484-8

1137

the dataset creation process. Section 4 presents an

exploratory data analysis to understand in detail the

dataset and its potential, using classiﬁcation and clus-

tering techniques; this section also discusses a differ-

ent analysis regarding the collected dataset based on a

deep learning approach. The paper ends at Section 5,

where the conclusion and future work are presented.

2 RELATED WORK

Identifying depression using artiﬁcial intelligence is

not a new research area. During the last years, works

that handled aspects of depression, such as identiﬁ-

cation, degree estimation and treatment monitoring,

through Machine Learning or Deep Learning tech-

niques have been produced.

De Choudhury et al(De Choudhury et al., 2013)

was a pioneer to describe how to construct a classi-

ﬁer to identify Twitter depressive users based on the

analysis of their activities in social media. That re-

search introduced social media as a source of data for

recognizing symptoms of depression in a user, using

measures as user engagement and emotion, egocentric

social graph, linguistic style, depressive language use,

and mentions of antidepressant medications, reaching

70% accuracy and precision of 0.74.

The work of Tsugawa introduced an approach to

identify the level of depression in Twitter users with

an accuracy of 69% (Tsugawa et al., 2015). Based

on the user’s historical activities, he constructed an

SVM model fed by topics inferred from word fre-

quencies. These words were supplied by volunteers,

which ﬁlled a questionnaire to analyse the degree of

depression, and the history activities were gathered

through the Twitter API.

Orabi presented an approach using the CLPsych

2015 Shared task data that considers Twitter posts

from authors to detect depression (Orabi et al., 2018).

His approach created different word vectors ap-

proaches (Skip-Gram, CBOW, Optimized and Ran-

dom) and applied these word vectors to different Deep

Learning architectures, such as a Convolutional Neu-

ral Network (CNN) and Recurrent Neural Network

(RNN), reaching an accuracy of 87.957% and preci-

sion of 87.435% as better results.

The works of Arora (Arora and Arora, 2019), Zi-

wei (Ziwei and Chua, 2019), Tao (Tao et al., 2019)

and others, despite handling depression in Twitter us-

ing sentiment analysis, only consider the sentences

polarities (positive, negative or neutral).

All these works inspired and contributed with

some thoughts to our solution, but they do not con-

sider emotions as important information to identify

Figure 1: Plutchik’s wheel of emotions. Source (Maupom

and Isyutina, 2013).

depression, that is our contribution to this approach.

3 IDENTIFICATION OF

DEPRESSIVE PROFILES

People have in their personalities, characteristics that

distinguish them from others. Some people are shy,

others more enthusiastic or sarcastic in its remarks.

All these characteristics determine their personalities,

and according to discrete emotional theorists, this per-

sonality is present in our expressions - facial, speech,

dress - and can be described as a set of independent

and basic emotions.

In the literature, a well-known model proposed by

Ekman states that all emotion is composed of 6 basic

emotions: happiness, sadness, fear, anger, surprise,

and disgust (Ekman, 1992). For Plutchik, any senti-

ment is a combination of 8 basic emotions: Anger,

Anticipation, Disgust, Fear, Joy, Sadness, Surprise

and Trust, and can be mapped as a wheel in emo-

tions”. Additionally, Plutchik introduces a dimension

of intensity which differentiates all basic emotions ac-

cording to the intensity degree, introducing a 3D emo-

tional model, as exhibited in Fig. 1.

3.1 Emotional Proﬁle

When handling with emotions, there is common

thinking that exists a straight connection between

emotions and mood, and it is true because mood ex-

presses emotions. However, the mood is different

from the emotional proﬁle: while the emotional pro-

ﬁle of a person stays rigid throughout our life, the

mood is more open to change, inﬂuenced by the cur-

rent emotions. Therefore, it is not possible to consider

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

1138

that the emotional proﬁle could be detected in a short

period. Moreover, when reminding the deﬁnition of

WHO about depression (depression can be longlast-

ing or recurrent), we must consider a relevant time

interval to detect the emotional proﬁle of an individ-

ual. Moreover the depression affects the emotional

proﬁle of a person, not his mood.

In this work, we considered the emotional pro-

ﬁle of an individual as the set of the eight Plutchik’s

basic emotions, in a distribution where the sum of

all these emotions results in 100%. We decided to

use Plutchik’s model because we believe that it repre-

sents better a way to handle the emotions and provides

mechanisms to differentiate levels of each emotion.

3.2 Data Sample Creation

Dealing with depression in social media requires the

ability to identify a pattern in the writings of some-

one who is suffering. However, as well as depression

is known as the “silent killer” illness, it is very difﬁ-

cult to ﬁnd data on social media posted by clinically

identiﬁed people with depression. For this reason,

we have adopted the strategy of searching in Twitter

all messages containing the text “I have depression”

in English. The objective of this approach is to col-

lect messages from authors self-declared depressives

for training a model using Natural Language Process-

ing and Machine Learning to identify the depressive

emotional proﬁle. Of course that this approach can

lead some undesired messages or some “fake depres-

sive messages” - i.e. people posting that has depres-

sion but it is not true. For this reason, in this study,

we consider that all messages in Twitter are veridi-

cal (i.e. they are not ironic and not lying) and each

author which posted “I have depression” in Twitter

is a potential depressive. However, to minimize the

possibility of fake depressive authors, we decide to

perform an emotional proﬁle validation across a re-

liable source. An example of a collected tweet is pre-

sented in Fig. 2.

Figure 2: Example of tweet collected.

The results from Twitter were manually analysed to

identify in which context the sentence was mentioned,

the authors of these sentences classiﬁed as “depres-

sives”, and they had all their tweets collected in a time

interval of 9 months.

For identifying non-depressive authors, we col-

lected messages from aleatory authors (initially iden-

tiﬁed these authors searching authors who posted any

sentence containing the aleatory word “tree”) in a

time interval of 9 months, as performed for depres-

sive authors.

During the tweets gathering, we discarded all re-

tweets and tweets containing mentions or links. This

is because we intend to detect the emotional proﬁle of

the depressive author with no interferences, i.e., we

wanted the messages those are not a chat with others,

advertisements, neither posts from an inﬂuencer (in

case of re-tweets).

The data sample resulted in 622339 tweets of

10416 different proﬁles.

3.3 Tweets Preprocessing

After collecting a set of tweets messages from self-

declared depressive authors (128745 tweets from

3890 authors) and non-depressive authors ( tweets

from 6526 authors), the next step consisted of a text

preprocessing pipeline to remove unnecessary infor-

mation from the texts. This pipeline, as presented in

Fig. 3 have their steps as:

• Tokenization: converts the texts into a list of sin-

gle words, or tokens;

• Part of Speech: processes all messages for gram-

matical identiﬁcation and cleaning all tokens

those grammatical categories are different from

nouns, verbs, adverbs and adjectives;

• Lemmatization: identiﬁes the lemma for each to-

ken and saves it for later use;

• Undesired words removal: removes all words

contained in a “blacklist” (stopwords) and those

with 3 or fewer characters.

Figure 3: Preprocessing tasks.

The ﬁnal result of this pipeline (therefore called pre-

processed text) is a new sentence containing only the

lemmas of words contained in tweets texts classiﬁed

as verbs, adverbs, adjectives and nouns, containing at

least 4 characters.

All preprocessing steps were developed under

Python using the Spacy

module for automatizing the

processes. We decided to use Spacy due to our expe-

rience in using this library.

https://spacy.io/

Identifying Depression Clues using Emotions and AI

1139

3.4 Sentiment Analysis

When the preprocessing is ﬁnished, the following step

in the dataset creation was to analyse the emotions

contained in the preprocessed texts. Regarding this

purpose, we developed a tool in Python that identi-

ﬁes the frequency of each word on the preprocessed

text and compares it with an emotional lexicon. The

existence of the word into the lexicon results in the in-

formation of the word has an emotional context. Us-

ing the NRC Emotion lexicon (Mohammad and Tur-

ney, 2013) and NRC Affect Intensity lexicon (Mo-

hammad, 2018), we identiﬁed the frequencies of all

emotional words according to Plutchik’s emotional

deﬁnition and extracted the distribution of each emo-

tion in a text. Regarding the affect intensity, the ap-

proach was to sum all intensities of each emotion for

each preprocessed text.

Later, we identiﬁed the mean emotional proﬁle

from each Twitter depressive author (TAMEP) to val-

idate the depressive authors (and avoid the fake self-

identiﬁed depressive). This measure is calculated

by the normalization of each emotion (summation of

each emotion divided by the sum of all emotions).

It results in a 12-dimension vector having values be-

tween 0 and 1, representing the average of each emo-

tion/intensity in a post.

For the emotional proﬁle validation of depres-

sive authors, we created a depressive emotional base-

line using the solution presented by Kim (Kim et al.,

2020), that analysed Reddit’s community r/depression

as a source for depressive texts. In our case, we iden-

tiﬁed the mean emotional proﬁle (RMEP) of depres-

sive posts from the last 600 Reddit’s posts and per-

formed the same steps described earlier to create the

emotional proﬁle validator.

To validate if a Twitter depressive author is a valid

depressive proﬁle, we performed a hypothesis test for

each author having TAMEP and RMEP. When the

null hypothesis could not be rejected (i.e., the p-value

between TAMEP and RMEP was less than 0.05), the

author proﬁle in TAMEP was considered as depres-

sive.

For the other hand, to identify the non-depressive

proﬁles, we adopted a solution of measuring the co-

sine distance between the non-depressive authors in

TAMEP and RMEP. Once each emotional proﬁle is a

12-dimensional vector (each Plutchik’s basic emotion

+ intensities), we deﬁned as non-depressive proﬁle the

ones that cosine distance between TAMEP and RMEP

is less or equal to 0.

This approach resulted in a set of 1788 authors

(947 classiﬁed as non-depressive and 841 classiﬁed

as depressive) and 492178 tweets collected.

Finally, we created a dataset containing the mean

of all emotions and intensities grouped by author and

trimester, where each author must have posted at least

150 messages. This minimal limit of 150 messages

was deﬁned empirically because it is necessary to col-

lect as many messages as possible, so, authors having

less than this amount could bias the analysis due to

less information about their emotions.

This approach - known as bag-of-words - was

adopted because we intended to identify which emo-

tions and intensities are relevant to detect depression,

besides creating a “depressive emotional ﬁngerprint”

through the words used in the texts.

Some recent NLP techniques - such as Word Vec-

tors and Transfer Learning - were discarded for this

study due to the nature of the problem detection in

the real world. Since a depressive person can be char-

acterized by negative emotions in their comments in

almost all situations in their lives, these techniques,

which can capture the context better than a bag of

words, are not relevant because the context of the

sentences is not relevant too. We consider that when

someone is depressive, their words are loaded of neg-

ative emotions in all situations, and for this reason,

it is relevant an author’s emotional proﬁle snapshot

over the time - exactly as a psychologist does during

the treatment.

The ﬁnal result was a dataset containing 1250 reg-

isters, divided into 686 non-depressive and 564 de-

pressive authors, indicating the average of emotions

and intensities of all your posts during the trimester.

To avoid problems with data unbalanced, we decided

to remove 11 aleatory non-depressive authors, result-

ing in 564 depressive and non-depressive authors. An

overview of the dataset is presented in Fig. 4.

4 DATA ANALYSIS

The data analysis was divided into two different anal-

ysis: Exploratory Analysis, and Machine Learning-

based analysis.

4.1 Exploratory Analysis

Exploratory analysis is the approach aimed at

analysing datasets to summarize their main features,

often with visual methods, aiming to ﬁnd pieces of

information hidden in the data.

In this work, the initial approach in the ex-

ploratory analysis was to identify if the emotional data

are represented by a normal distribution. For this pur-

pose, we performed a Shapiro-Wilk test for each emo-

tion and intensity in the dataset. During our tests, all

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

1140

Figure 4: Dataset created.

p-value results were less than 0.05, conﬁrming that

the data are represented as a normal distribution.

Later, the outliers in each emotion and intensity

were identiﬁed to remove them from the sample. For

this reason, we created a boxplot to identify visually

the range of each dataset’s dimension and their re-

spective number of outliers, as presented in Fig. 5.

Each emotion was considered as an outlier when its

value was out of the range [-2σ ≤ x ≤ 2σ].

Figure 5: Outliers identiﬁcation.

Regarding outliers handling, our approach was to

change each outlier value for the mean of each

emotion or intensity category (depressive or non-

depressive). The reason was to avoid that outliers in

some emotion (for example) affect others in case of

removing the entire register.

The next step was to identify the relationship be-

tween the dataset dimensions and the depression indi-

cation. For this purpose, we measured the correlation

between the depressive ﬂag and the emotions, as pre-

sented in Table 1.

These outcomes show that negative emotions

(anger, fear and sadness) have a moderated correla-

tion (r

) with depression, whereas positive emotions

(in this case, anticipation and joy) have an inverse

moderated correlation. The same analysis was per-

formed for the emotional intensities, as presented in

Table 2, conﬁrming the same results.

Despite the data values analysed in the correla-

tion are different in their characteristics - emotions

have continuous values while the information about

depression has a nominal value - the Pearson corre-

lation can be applied because these data can be in-

terpreted as a biserial correlation. This is justiﬁed

because the depression indicative can only have 2 val-

ues, assuming binary characteristics.

4.2 Clustering Analysis

The objective of the Clustering Analysis is to per-

form data transformations to understand if and how

the data can be grouped. To achieve this, the initial

step was to transform the 12-dimension dataset into

a 2-dimension dataset for visualizing the data as a

scatter plot, and this would be impossible using 12-

dimension data.

Initially, we performed a Principal Component

Analysis (PCA) to reduce the dataset (with no infor-

mation about depressive classiﬁcation) dimensional-

ity. The PCA algorithm identiﬁed that the most rele-

vant dimensions in the dataset were Fear and Disgust,

which can contain 72.33% of the information.

Next, the dataset resultant from PCA analysis fed

the KMeans algorithm used to cluster into two cate-

gories the data and generated a scatter plot. The re-

sultant graphic is presented in Figure 6.

Figure 6: Clusters of information.

This information is important because it shows

that the emotional data about depressive and non-

depressive can be divided into 2 distinct categories,

visually distant, and reinforced the WHO’s deﬁnition

about depression (“depressive disorders are charac-

terized by sadness, loss of interest or pleasure, feel-

ings of guilt or low self-worth, disturbed sleep or ap-

Identifying Depression Clues using Emotions and AI

1141

Table 1: Correlations between depressive status and basic emotions.

Basic emotions

Anger Anticipation Disgust Fear Joy Sadness Surprise Trust

0.47 -0.43 0.17 0.45 -0.56 0.32 -0.28 -0.19

Table 2: Correlation between depressive status and intensi-

ties.

Intensities

Anger Fear Joy Sadness

0.49 0.49 -0.43 0.41

petite, feelings of tiredness, and poor concentration”)

because it shows that emotions fear and disgust are

relevant to identify depression.

4.3 Machine and Deep Learning

Analysis

The next analysis consisted of the creation of a clas-

siﬁcation model able to identify the depressive emo-

tional proﬁle in messages. To achieve this objective,

we used the dataset created in section 3.4 in some Ma-

chine Learning and Deep Learning algorithms, aim-

ing to identify the best model to classify depression.

Some characteristics from the problem (depres-

sion classiﬁcation) and the dataset - as few examples -

were relevant in the choice of the algorithms analysed.

Algorithms like LDA (Linear Discriminant Analysis)

and Fisher Linear Discriminant were discarded be-

cause we believe that the problem is not represented

by a linear function.

So, in our analysis, the following algorithms were

considered: Support Vector Machines (SVM), Ran-

dom Forests, Naive Bayes, DNN and 1-D CNN. In

our analysis we just considered the algorithm’s in

their standard conﬁguration and having as input each

emotion and intensities, in a dataset’s division of 30%

for testing and 70% for training. For the Deep Learn-

ing models, we created some different model archi-

tectures using Tensorﬂow to evaluate the better re-

sults. For DNN, the architecture whose got the best

precision was a 5-layer neural model (12-12-8-6-2

layers), having a dropout of 0.5 between each tier to

avoid overﬁtting, respectively. Regarding CNN archi-

tectures, the most accurate was a 5-tier 1-D Convo-

lutional Neural Network, using the same strategy of

dropout 0.5 on each tier to avoid overﬁtting.

The precision and mean squared error (MSE) for

each algorithm with its best result and network archi-

tecture is presented in Table 3.

As a baseline comparison, we performed classiﬁ-

cation using the same algorithms and the same con-

ﬁguration to identify the inﬂuence of emotional la-

Table 3: Benchmark of Machine and Deep Learning algo-

rithms.

Algorithm Precision MSE Recall

SVM 0.984 0.126 0.858

Random Forest 0.8 0.176 0.869

Naive Bayes 0.76 0.45 0.745

DNN 0.939 0.076 0.793

CNN 0.915 0.071 0.746

bels on the classiﬁcation. As input values, all tweets

were transformed to 12-dimension word vectors us-

ing Word2Vec trained for 50 epochs. To calculate

the word embedding representation for each tweet, we

calculated the mean of all 12-dimension vector words

contained in the message. The resulting vector is con-

sidered the 12-dimension representation of the sen-

tence. The results of each algorithm are presented in

Table 4.

Table 4: Benchmark of Machine and Deep Learning algo-

rithms - Word2Vec.

Algorithm Precision MSE Recall

SVM 0.642 0.463 0.642

Random Forest 0.636 0.411 0.509

Naive Bayes 0.731 0.56 0.74

DNN 0.836 0.351 0.698

CNN 0.779 0.221 0.764

These results show that some Machine Learning al-

gorithms such as SVM and Deep Learning algorithms

as DNN can identify the depressive emotional pattern

with good precision. These results are better than the

results observed in Section 2, and the values obtained

in Table 4, reinforcing that the emotional text analysis

to identify depressives can be a promising alternative

to help people that are suffering silently.

5 CONCLUSION

Day after day, depression is becoming an epidemic

disease that affects people of different social levels,

cultures and ethnicities. Due to the nature of silence,

identifying people who ask for help because of this

illness but cannot verbalize that request is quite com-

plicated, and often goes unnoticed even by the person

suffering from depression.

The use of textual sentiment analysis can help

ICAART 2021 - 13th International Conference on Agents and Artiﬁcial Intelligence

1142

identify the disease as it is a non-invasive method that

can be continuously monitored. This is a huge help

in the war against depression because it enables us to

identify periods of wellness and sadness without a ne-

cessity to visit a psychologist, enabling a quick action

when necessary.

Despite many works in this research area, the re-

sults obtained for this emotional model to identify de-

pression are promising when compared to the previ-

ous efforts, principally when the precision of 0.984 on

depression classiﬁcation is presented. However, once

the information was data collected from social me-

dia, we cannot discard the hypothesis of biased data,

because it is not possible to assure that the authors

were true when writing their posts. However, we be-

lieve that this approach when using texts from clin-

ically depressive authors - as the psychologists do -

tend to conﬁrm the depression diagnostic identiﬁed

by the psychologist.

As future work, we plan to use this approach

on clinically identiﬁed depressive patients, to iden-

tify their emotional proﬁles and conﬁrm that the

precision-based in social data remains when using

data collected from a controlled situation, as an ap-

pointment with the psychologist.

ACKNOWLEDGEMENTS

We would like to thank to Loggi

data team for all in-

teresting discussions about metrics and proﬁle identi-

ﬁcation.

This work has been supported by FCT – Fundac¸

para a Ci

encia e Tecnologia within the R&D Units

Project Scope: UIDB/00319/2020.

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