The proliferation of online social media has recreated the
way people interact with each other. Online social
networking sites like Facebook, Twitter, Reddit, and
others provide an excellent platform of communication for
users willing to convey their feelings, emotions, and
sentiments regarding multiple topics or issues. From the
user's perspective, online social media gives users to
communicate and contribute to any case freely, and from
the researchers' perspective, it provides deep insight about
what could be the mental state of a user while they have
interacted with any topic. To get more insights regarding
these topics, machine learning techniques can be quite
helpful for identifying the unique hidden features in this
online communication and revealing the individual's
corresponding mental state. In a survey by WHO, India,
China, and the USA came out to be the terrible sufferers
of anxiety, schizophrenia, and bipolar disorder [20].
Although effective treatment for mental diseases is
available, only 10% of people take this treatment, and the
majority is unable to approach this treatment because of
the stigma associated with mental health. Other factors
associated are inaccurate assessment, untrained healthcare
professionals, and lack of education and resources. With
the easy accessibility and enormous amount of data
available, users tend to use the Internet to share and gather
health-related information. In 2009, Pew Research Center
published a report showing that 61% of adult internet users
search for medical information online [3]. When any user
or their close one is encountered with any medical
condition, they start searching for it online by comparing
the information available on multiple sources online [2].
Users' most searched topics are medication, side effects,
symptoms, and healthcare professionals [9].
Looking from the patient's perspective, information that is
available for users online serves two purposes. In the first
scenario, users generally look for symptoms and
treatments for mental health disorders and then diagnose
themselves based on the information available. These
solutions are convenient for users with little or no access
to healthcare. On the other hand, users can have a deeper
understanding of their medical condition while interacting
with online medical health communities. But not all online
communities cater all the information about the disorders,
so sometimes people misdiagnose their health disorders.
Lykke et al. showed that the queries used by experts and
the naïve users for searching for medical information are
remarkably different, which can make a user end up with
the wrong diagnosis information [10]. When
misinformation reaches a user, they can become more
depressed and can end up in depression [7]
Reddit is an online social media platform used to seek and
give healthcare advice on various topics and issues. Reddit
allows users to freely and honestly share their concerns as
their identity is hidden, and there is no stigma on social
media regarding this as can be seen in fig:1 where a social
media user on Reddit is expressing himself/herself by
asking for an opinion/thought on his/her post in the mental
health subreddit.
Fig: 1 Snapshot of a subreddit post by a user on the Reddit Platform.
Measuring the Effectiveness of LDA-Based Clustering for Social Media
Data
AYSHA KHAN, RASHID ALI
Department of Computer Engineering ZHCET, Aligarh Muslim University Aligarh, INDIA
Abstract: Social media has come out as a great platform for users to communicate and share their opinions, photos, and
videos that contemplate their moods, feelings, and emotions. This wide variety of data provides multiple possibilities for
exploring social media data to investigate feelings and sentiments based on their moods and attitudes. With the enormous
increase in mental health disorders among individuals, there is a massive loss in productivity and quality of life. Social
media platforms like Reddit are used to seek medical advice on mental health issues. The structure and the content on
various subreddits can be employed to interpret and connect the posts for mental health diagnostic problems. In this work,
we have focused on seven mental health disorders, namely Anxiety, Depression, Bipolar, Autism, Borderline personality
disorder, Schizophrenia, and mental health, which are actually subreddits posted by users on the Reddit social media
platform. In this work, we have measured the effectiveness of topic modeling using Latent Dirichlet Allocation on these
social media posts to identify the most used words and discover the hidden topics in their posts and also analyzed the results
on evaluation metrics based on perplexity and coherence scores on unigrams, bigrams, and trigrams.
Keywords: Reddit, latent dirichlet allocation, topic modeling, mental health disorders, bigrams, trigrams, social media
mining
Received: March 14, 2022. Revised: October 19, 2022. Accepted: November 24, 2022. Published: December 31, 2022.
1. Introduction
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When patients interact with practitioners about their
mental health issues, the content shared having the
questionnaire, and the interview is biased as they are
organized in a clinical environment [12]. For more
additional insights, the social media information of the
user can also be linked with the above two traditional
techniques. This social media content will help extract
meaningful information about the attitude and awareness
of the patient regarding their condition and what kind of
treatment they need. However, accessing and using such
social media information generally requires the users'
consent to maintain their confidentiality. In this way,
healthcare can be merged with social media, and health
practitioners can aggregate this data to give personalized
treatment to the users. This platform is persistent for
conversations related to mental health. Its subreddits are
discussion forums committed to specific discussions
created by the users. Some of the famous subreddits for
mental health are "r/mentalhealth" and "r/mentalillness"
with more than 100k members each.
However, using such information requires de-
identification and the consent of patients following the
patient confidentiality requirements. In this case, a patient
can provide their handle on Reddit and permission for the
specified use, and their content can be collected and
analyzed to predict the presence and progression of mental
health conditions. Thus, a mental health professional
(MHP) can aggregate various signals and personalized
insights from diverse sources, including patient Electronic
Health Records (EHR), questionnaires, interviews, and
social media.
The inspiration behind this work is the immense popularity
of online social media among every age group. This gives
rise to a tremendous amount of data available on such
platforms. This data can give us insights into various
aspects of people's lives. To the best of our knowledge, we
are the first to use topic modeling for mental health-related
posts on a social media platform. Most of the papers we
came across focus on the classification of social media
posts. This paper proposes an LDA-based topic modeling
technique using subreddits from the Reddit social media
platform with seven classes. This work generally focuses
on topic modeling with discovering the hidden patterns in
the insightful data of the users. After performing various
analysis, LDA is evaluated with multiple evaluation
metrics.
The remaining paper is organized as follows: Section 2
describes the related work, followed by section 3 of the
Preliminaries needed to understand our system. Section 4
describes the methodology, followed by section 5 with the
conclusion.
Twitter data was leveraged to predict the depressed users'
intensity level from no to severe depression. They labeled the
data weekly in a self-supervised way. The features were
extracted on different levels ranging from emotional, topical,
behavioral, user-level, and depression-related n-gram
features for representing each user, and these features were
then fed into a Long short-term memory (LSTM) network.
With the experiments, it was discovered that depressed users
tend to use words like stress and sad. They have a pattern of
posting late at nights using personal pronouns. They
compared their model with SVM, DNN, and GRU and
claimed that their model performed the best [18].
Minjoo et al. analyzed subreddit communities for bipolar and
depressive disorders to study how users share their illnesses
and seek advice. A semantic network analysis was performed
on this data and showed that users of both diseases had sleep
disorders and financial problems. It was also discovered that
users with bipolar disorder were more curious about
medication, while users with depression had more interest in
suicide. LIWC dictionary was used, and mean scores were
calculated on four indicators: analytical thinking, clout,
authenticity, and emotional tone. [15].
Han et al. worked on the Big-Five model to identify
personality traits. The authors developed a Big-Five
questionnaire system for 60-item personality inventories with
the IDs of the users. Initially, personality-related keywords
are obtained from the user's microblog, then word
embeddings are applied and fed into a clustering algorithm.
These words forming into these clusters are given different
classes defining a semantic concept [6]. Park et al. [13]
examined the language used on Twitter to see if it indicated
depressive moods, attitudes, and behaviors. They also
performed face-to-face interviews to identify the
interdependence between these interviews and Twitter data.
Tsugawaet et al. [17] leverage tweets from Twitter to
investigate the posting behavior of Japanese-speaking users
to predict depression among them. Xue et al. [19][20]
explored social media data for stress detection from tweets of
teenagers by first detecting their psychological pressures and
then assisting them in their stress through microblogs.
Trotzeket et al. [16] gave a deep learning-based framework
for proactively detecting depression from social media data.
They used different word embeddings for their CNN model
and compared it with user-level-linguistic metadata-based
classification. Shen et al. [4] built a dataset on depression
having depression and non-depression labels on Twitter by
extracting six depression-related features covering clinical
depression and online behavior on social media.
Zhancheng et al. developed a multi-label personality
detection model using BERT on the MTBI and the Big-Five
dataset by combining the semantic and emotional features on
CNN, LSTM, and GRU. They evaluated their models on
different datasets [21]. Majumdar et al. used word
embeddings to analyse the semantics of the 2457 essays using
convolutional neural networks [11]. In the past years, a new
technique for word embedding has been developed i.e.
transformers. Keh et al. used Bert embeddings for a
personality detection model from social media data on
Twitter and found improved accuracy in their work [8].
A mental health disorder is characterized by a wide variety of
mental illnesses that can take hold of an individual's mood,
thinking, and behavior. In today's world, most people are
involved with mental health, and it becomes a matter of
concern when it persists continuously, causing untimely
2. Motivations and Contributions of Paper
3. Related Work
3.1 Using Social Media for Text Analysis 4. Preliminaries
4.1 Mental Health Disorders
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stress and hampering the ability to function. Mental health
can affect any person of any age. The symptoms of mental
illness can be treated by combining medication and therapy.
According to a survey conducted by National Alliance on
Mental Illness, every 1 in 5 adult experience mental illness
every year in the U.S. In another study by the National
Alliance on Mental Illness [23], 50% of mental illness begin
by the age of 14, and 75% starts by the age of 24. In this
research, it was found that mental health disorder is not the
result of any single incident, it is the outcome of multiple
factors like genetics, history of psychological or physical
abuse, brain injury, consuming alcohol and recreational
drugs, environment, lifestyle, biochemical processes,
chemical imbalances in the brain are the factors associated
with it [24].
The different mental health disorders are Anxiety disorders,
Bipolar disorder, Borderline Personality disorder,
Depression, Obsessive-Compulsive disorder, Post Traumatic
Stress Disorder, Psychosis, Schizoaffective disorder,
Schizophrenia etc.
N-Grams are defined as a contiguous sequence of n items that
are initiated from a sample of text. This n can be any number
1,2,3 and so on.
• Unigrams: When the value of n is 1, then it is a
unigram. It is the most commonly used approach
and generally presents the words as single tokens.
This method doesn’t take into consideration any
meaning of the words.
• Bigrams: When the value of n is 2, then it is a
bigram. In this approach, two tokens are presented
from the dataset.
• Trigrams: When the value of n is 3, then it is a
trigram. In this method, three tokens are presented
simultaneously for the document.
Latent Dirichlet Allocation (LDA) is a language model for
topic modeling based on clustering. It is an unsupervised
technique for analyzing text. It is based on probabilistic
distribution, which tells us the topics hidden in the
documents. LDA gives two sets of probability distributions
as a result. Firstly, for each document, it gives a topic set of
distribution. Secondly, for each topic, it gives a word set of
distributions [1] as can be seen in fig: 2.
Fig: 2 Architecture of Latent Dirichlet Allocation
Here, the parameters of LDA are described as follows in
fig:2:
D: the number of documents.
Nd: no. of words in a given document.
β: dirichlet prior on the per-document topic
distribution.
α: dirichlet prior on the per-topic word distribution.
θi: topic distribution for document i.
φk: word distribution for topic k.
zij: topic for the jth word in document i.
wij: specific word.
There are three essential hyperparameters for LDA:
• Document-topic density (α): It tells us the exact
number of topics expected from the documents. If
the value is smaller, then fewer topics will be given.
If the value is greater, then more topics will be
given.
• Topic-word density (β): It tells us about the word
distribution in the topics. If it is smaller, then there
are fewer words in the topic. If the value is greater,
then more words are in the topic.
• Number of topics (K): This value tells us about the
number of topics you want from the documents.
This value depends on the domain in which you are
working. Testing the different values of k can give
better insights into the compositions.
For evaluating our topic modeling model LDA, we use
evaluation metrics like the Perplexity and the Coherence
score. It helps us understand how explainable the topics are
to humans. Topics are basically represented as the top N
words with the highest probability of being associated with
the particular topic. In other words, it is the measure of the
similarity of these words with each other.
1) Perplexity:
It is the metric for evaluating how successfully the trained
model predicts the new data. It is defined as the decreasing
function of the likelihood of new documents. Perplexity
decreases with the increase in the likelihood of the words that
are appearing together. A good topic is identified with a low
perplexity score [24]. A low perplexity score says that it has
predicted the words correctly in the documents. However,
perplexity and human-judgment can differ in the same results
and are often not correlated.
2) Coherence score:
It is defined as the measure of calculating the degree of
semantic similarity between the high-scoring words in the
topic. In this work, we have used the cv coherence score
which generates content word vectors using their
cooccurrences. For calculating the score, it uses cosine
similarity and normalized pointwise mutual information
(NPMI). It is one of the most popular and default metric
techniques in the genism pipeline of python. It is denoted as
c_v in genism [25]
Mental health disorders impact people's lives in a way that is
not easily identifiable. Like physical disorders, mental health
disorders are not visible from the outside, and thus it becomes
difficult to identify them quickly. But with the rise of social
media, it has become easier to look at such disorders. In this
4.2 N-Grams
4.3 Latent Dirichlet Allocation (LDA)
4.4 Evaluation Metrics
4.5 Problem Definition
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work, we have tried to work on social media posts of users on
mental health disorders among people on the popular social
media platform Reddit. We obtained the subreddits for
Anxiety, Depression, Mental Health, Bipolar Disorder,
Borderline Personality Disorder, Autism, and Schizophrenia
[5]. The problem here is to identify the most discussed topics
among the posts of these social media users on these
particular subreddits. The main intention of using Latent
Dirichlet Allocation (LDA) is that it is the most widely used
topic model which helps in identifying the hidden topics in
the text documents.
Fig: 3 System architecture of our work
For our work, we gathered data from the Reddit platform
consisting of subreddits posted by various users. Initially, the
dataset consisted of unstructured text and unwanted noise so
this data was pre-processed. After that, features were
extracted from the corpus. We have tried to build LDA on
unigrams, bigrams, and trigrams. Then, these features were
fed into the LDA-based model and are evaluated on different
evaluation metrics, and finally the topics are generated as can
be observed in fig: 3.
In this work, we have used the Reddit dataset consisting of
subreddit posts of users with seven mental health conditions.
These mental health disorders are Anxiety, Schizophrenia,
Bipolar disorder, Depression, Borderline Personality disorder
(BPD), Autism, and Mental health [5] with a total of 227122
posts as shown in table 1. As can be seen in table 1, the total
number of posts corresponding to each subreddit is presented.
Table 1: Subreddit with the total number of posts
Subreddit
Number of posts
Anxiety
25881
Autism
3572
Depression
129308
Bipolar
20759
Borderline Personality
Disorder
19134
Schizophrenia
8769
Mental Health
19699
Total posts
227122
Table 2: Sample of a subreddit from Anxiety Disorder
Text
I have struggled with social
anxiety from childhood and
the main advice from
friends, self-help books and
professionals is to expose
myself to those
environments but I'm not
better off!
Anxiety
Reddit allows users to post their content in different
subreddits available. From Table 2, it can be observed that
the user has posted about his/her anxiety in the Anxiety
Subreddit. For our LDA, model we have not used the class of
the particular subreddit, we only worked on the text available
in the documents.
Initially, the dataset obtained was containing some erroneous
characters. We saved the dataset in CSV files and used utf-8
encoding to remove that. Fig 4 shows us the word cloud of
the words most prominent in our mental health dataset. We
can see terms like people, know, feel, friend, want, life,
depression, anxiety, and problem.
Fig: 4 Word cloud of Reddit dataset
In our work, pre-processing has been performed in multiple
steps. First, the text is cleaned for unnecessary special
characters. Then punctuations are removed, and then finally
the dataset is converted into the lower text. In the next step,
the stopwords are removed as shown in table 3. In the next
step, the data is tokenized, and then unigrams, bigrams, and
trigrams are created. In the final step, the data is lemmatized
separately for each unigram, bigram and trigram. For this pre-
processing, the python libraries used are spacy, genism, and
NLTK.
Table 3: Example of data pre-processing on the subreddit
After-preprocessing
i have struggled with social anxiety main advice from
friends self help books professionals expose myself those
environments i am not better off
High dimensional feature detection and extraction can
perform better as it is one of the most significant steps of text
mining [13]. The features that are extracted here are the
unigrams, bigrams, and trigrams. In table 4, there is a glimpse
5. Methodology
6. Experiments and Results
6.1 Dataset 6.2 Data Pre-processing
6.3 Feature Extraction
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of how these are generated for our sample text shown in table
4.
Table: 4 samples of unigram, bigram, and trigram for our text
Text
i have struggled with social anxiety main
advice from friends self help books
professionals expose myself those
environments i am not better off
Unigrams
‘i’, ‘have’, ‘struggled’, ‘with’, ‘social’,
‘anxiety’, ‘main’, ‘advice’, ‘from’, ‘friends’,
‘self’, ‘help’, ‘books’, ‘professionals’,
‘expose’, ‘myself’, ‘those’, ‘environments’,
‘i’, ‘am’, ‘better’, ‘off’
Bigrams
‘i have’, ‘struggled with’, ‘social anxiety’,
‘main advice’, ‘from friends’, ‘self help’,
‘professionals expose’, ‘myself those’,
‘environments i’, ‘am better’, ‘off’
Trigrams
‘i have struggled’, ‘with social anxiety’,
‘main advice from’, ‘friends self help’,
‘professionals expose myself’, ‘those
environments i’, ‘am better off’
Once the n-grams are generated, then a corpus is created for
each of the n-grams structure using tf-idf which is then fed
into the LDA model. The LDA model was run for k=7
number of topics, 10 number of passes with a chunk size of
100, a random state of 200, and default values for alpha and
beta hyperparameters. The model gave top words for each
topic in unigrams, bigrams, and trigrams which can be seen
in table 5, table 6, table 7 respectively. In table 6, in topic 1,
top words like get, life, depression, think, back and stress is
prominent, which can be concluded to align with the
depression class.
Table: 5 Top five words from seven topics for unigram
Topics
Top Words
Topic 0
Feel, never, more, month,
live
Topic 1
Get, think, start, life,
depression
Topic 2
Want, now, see, work, year
Topic 3
Time, bad, day, sleep,
anxiety
Topic 4
Tell, say, give, find, sad
Topic 5
Go, make, try, people,
leave
Topic 6
Help, thing, close, die,
reason
Table 6 : Top five words from seven topics for bigram
Topics
Top Words
Topic 0
Attack, panic, exposure,
stone, diagnosis
Topic 1
Get, life, depression, think,
stress
Topic 2
Time, bad, friend, anxiety,
sleep,
Topic 3
Autistic, therapy, learn,
problems, birth
Topic 4
Therapista, environment,
mood, life, bipolar
Topic 5
Struggle, throwaway, bpd,
cry, work
Topic 6
Table: 7 Top five words from seven topics for trigrams
Topics
Top Words
Topic 0
Attack, panic, conversation,
anxiety, time
Topic 1
Make, wake, medicine,
social, bipolar
Topic 2
Autism, one, birth, issues,
therapy
Topic 3
Feel, like, life, time,
depression
Topic 4
Mood, life, environment,
bipolar, sleep
Topic 5
Struggle, throwaway,
hard,lose, mental
Topic 6
Paranoid, step, little, last,
something, attack
Table: 8 Evaluation metrics for n-grams
No. of topics
Perplexity
Coherence Score
Unigrams
-8.17
0.25
Bigrams
-7.45
0.78
Trigrams
-7.06
0.72
For evaluating the topic models, perplexity and coherence
scores are used. Table 4 shows us the evaluation metrics of
the LDA model for number of topics (k = 7). Here, we have
generated perplexity and coherence scores for unigrams,
bigrams, and trigrams. As we mentioned earlier, negative
perplexity is better, so for our model, it is best for unigrams
i.e. -8.176. It can also be observed from table 8, that the
perplexity is not much varied for unigrams, bigrams, and
trigrams which clearly implies that perplexity is not a good
measure for the n-gram features as they are almost close to
each other. Whereas, the higher the value of the coherence
score, the better it is. Here, it is best for bigrams i.e. 0.78. As
unigrams are just a tokenized version of our text, so all the
meanings are generally lost and because of that we got a very
low coherence score of 0.25 for unigrams. Coherence score
for bigrams and trigrams is very close to each other which
implies that coherence score is a good measure.
Fig: 4 Evaluation metrics for unigram, bigrams, trigrams
Here, in fig 4, perplexity and coherence scores for unigrams,
bigrams, and trigrams are presented in graphical format. As,
perplexity is generally negative it can be seen in a downwards
-10
-8
-6
-4
-2
0
2
Unigram Bigram Trigram
Evaluation Metrics
Perplexity C_V
6.4 LDA Results
6.5 Evaluation Metrics
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direction. The coherence score for unigram, bigram, and
trigram is positive so it is upwards.
Mental health is impacting individuals worldwide. Finding a
solution to combat it before it lets a person commit suicide is
of utmost importance as it is still considered taboo to talk
about it and consult a therapist for such problems. With the
advent of social media, it is easy for people to share their
issues with others, be it friends, healthcare professionals,
experts etc. In this work, we proposed a novel topic modeling
technique to find the inherent groups in individuals' mental
health disorder datasets. The motive of the study is to identify
the word usage of users on online social media platforms to
understand how individuals are perceiving and sharing their
experiences about mental health disorders. We applied the
topic modeling technique i.e. Latent Dirichlet Allocation to
understand these disorders. Therefore, using NLP techniques
combined with social media texts can help researchers get
better insights into the problems of individuals who cannot
share them with their healthcare professionals.
This work has many limitations which we can remove in the
future. Some of these are:
• When evaluating in terms of coherence score, we
have only considered the CV coherence score.
Although, there are more versions of the coherence
score available.
• In this work, we have only presented the top words
present in each topic only. We have not analyzed
which of the subreddits are falling into which class.
• We have compared this work with different n-gram
models using LDA.
In the future, we can also focus on improving this work. Some
of them are:
• Talking about coherence score, we have only
worked on one coherence score i.e. cv. There are
other variants of coherence scores also available on
which we are working.
• We have only focused on the Reddit platform. This
work can also be performed on other social media
platforms like Twitter, where users are vocal about
their issues and problems, and try to implement it
with deep learning models, and classify the different
subreddits in the given seven classes.
• We can also compare this work with other topic
modeling models Latent Semantic Indexing (LSI).
[1] David M. Blei, Andrew Y. Ng, and Michael I. Jordan, “Latent dirichlet
allocation.”, J. Mach. Learn. Res., vol. 3, pp. 993-1022, 2003.
[2] Fiksdal, A. S., Kumbamu, A., Jadhav, A. S., Cocos, C., Nelsen, L. A.,
Pathak, J., & McCormick, J. B., “Evaluating the process of online
health information searching: A qualitative approach to exploring
consumer perspectives”,Journal of Medical Internet Research, vol.16,
no. 10, 2014.
[3] Fox, S., “The Social Life of Health Information”, Retrieved from
http://www.pewresearch.org/fact-tank/2014/01/15/the-social-life-of-
health-information, 2014.
[4] G. Shenet al., “Depression detection via harvesting social media:A
multimodal dictionary learning solution,” inProc. 27th Int. Joint
Conf.Artif. Intell., pp. 3838–3844, Aug 2017.
[5] Gkotsis, G., Oellrich, A., Velupillai, S. et al. Characterisation of mental
health conditions in social media using Informed Deep Learning. Sci
Rep 7, 45141, 2017.
[6] Han, S., Huang, H., & Tang, Y., “Knowledge of words: An
interpretable approach for personality recognition from social media.
Knowledge-Based Systems”, Article 105550
[7] Johnson, J. D, “Health-related information seeking: Is it worth it”,
Information Processing & Management, vol.50, no.5, pp. 708–717,
2014
[8] Keh, S.S., .& Cheng, I.T., “Myers-Briggs personality classification and
personality-specific language generation using pre-trained language
models”, arXiv preprint arXiv.06333, 2019.
[9] Lawhon, L., “Patients Rely on Facebook and Condition-Specific Web
Sites to Share Information” , Get Support, and Start Discussions with
Healthcare Providers. Retrievedfromhttps://health-
union.com/news/online-health-experience-survey/, 2016.
[10] Lykke, M., Price, S., & Delcambre, L, “How doctors search: A study
of query behaviour and the impact on search results”, Information
Processing & Management, vol. 48,no.6, pp.1151–1170, 2012
[11] Majumder, N., Poria, S., Gelbukh, A., & Cambria, E, “Deep learning-
based document modeling for personality detection from text”, IEEE
Intelligent Systems, vol. 32, no.2, pp.74–79, 2017.
[12] Matthew R Jamnik and David J Lane., “The Use of Reddit as an
InexpensiveSource for High-Quality Data”, Practical Assessment,
Research & Evaluation, 2017
[13] M. Park, C. Cha, and M. Cha, “Depressive moods of users portrayedin
twitter,” inProc. ACM SIGKDD Workshop Healthcare Informat. (HI-
KDD), pp. 1–8, 2012
[14] Minjoo Yoo, Sangwon Lee, Taehyun Ha, “Semantic network analysis
for understanding user experiences of bipolar and depressive disorders
on Reddit”, Information Processing & Management,Vol. 56,
no.4,2019,Pages 1565-1575,
[15] Monalisha Ghosh; Goutam Sanyal, “Analysing sentiments based on
multi-feature combination with supervised learning”, International
Journal of Knowledge Engineering and Data mining, vol.11, no.4, pp.
391-416, 2019
[16] M. Trotzek, S. Koitka, and C. M. Friedrich, “Utilizing neural
networksand linguistic metadata for early detection of depression
indicationsin text sequences,”IEEE Trans. Knowl. Data Eng., vol. 32,
no. 3,pp. 588–601, Mar. 2020
[17] S. Tsugawa, Y. Kikuchi, F. Kishino, K. Nakajima, Y. Itoh, and H.
Ohsaki,“Recognizing depression from Twitter activity,” inProc. 33rd
Annu.ACM Conf. Hum. Factors Comput. Syst., pp. 3187–3196, 2015.
[18] S. Ghosh and T. Anwar, "Depression Intensity Estimation via Social
Media: A Deep Learning Approach," in IEEE Transactions on
Computational Social Systems, vol. 8, no. 6, pp. 1465-1474, Dec. 2021.
[19] Y. Xue, Q. Li, L. Feng, G. D. Clifford, and D. A. Clifton, “Towardsa
micro-blog platform for sensing and easing adolescent
psychologicalpressures,” inProc. ACM Conf. Pervas. Ubiquitous
Comput. AdjunctPublication, pp. 215–218, Sept 2013.
[20] Y. Xue, Q. Li, L. Jin, L. Feng, D. A. Clifton, and G. D.
Clifford,“Detecting adolescent psychological pressures from micro-
blog,” inProc. Int. Conf. Health Inf. Sci.Melbourne, VIC, Australia:
Springer, pp. 83–94, 2014.
[21] Zhancheng Ren, Qiang Shen, Xiaolei Diao, Hao Xu, “A sentiment-
aware deep learning approach for personality detection from text”,
Information Processing & Management, vol.58, no.3, 2021.
[22] India is the Most Depressed Country in the World,
https://www.indiatoday.in/education-today/gk-current-
affairs/story/india-is-the-most-depressed-country-in-the-world-
mental-health-day/-2018-1360096-2018-10-1, 2018 ,Accessed on May
8, 2022
[23] Mental Health Illnesses, https://www.nami.org/About-Mental-
Illness/Mental-Health-Conditions, Accessed on June 6. 2022
[24] Medline plus, https://medlineplus.gov/mentaldisorders.html, Accessed
on May 3, 2022
[25] Evaluate Topic Models: Latent Dirichlet Allocation (LDA), 2019,
https://towardsdatascience.com/evaluate-topic-model-in-python-
latent-dirichlet-allocation-lda-7d57484bb5d0, Accessed on June 6,
2022
7. Conclusion
8. Limitations and Future Work
References
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Engineering World
DOI:10.37394/232025.2022.4.11
Aysha Khan, Rashid Ali
E-ISSN: 2692-5079
90
Volume 4, 2022