=Paper=
{{Paper
|id=Vol-3416/paper_6
|storemode=property
|title=Stress Detection System using Natural Language Processing and Machine Learning Techniques
|pdfUrl=https://ceur-ws.org/Vol-3416/paper_6.pdf
|volume=Vol-3416
|authors=Kirti Kumari,Sima Das
|dblpUrl=https://dblp.org/rec/conf/icon-nlp/KumariD22
}}
==Stress Detection System using Natural Language Processing and Machine Learning Techniques==
https://ceur-ws.org/Vol-3416/paper_6.pdf
Stress Detection System using Natural Language
Processing and Machine Learning Techniques
Kirti Kumari1,∗ , Sima Das2
1
Indian Institute of Information Technology Ranchi, Namkum, Ranchi,Jharkhand, India.
2
Bengal College of Engineering and Technology Bidhannagar, Durgapur, West Bengal, India.
Abstract
Stress is one of the important phase of mental state where we feel emotional or physical tension. If we
do not find way to manage stress then it may leads to physical or mental health issues. The COVID-19
pandemic affected almost everyone into the stressed phase due to long duration of social distancing,
lockdown, fear, negativity, etc. The people used more online activity as an alternative way of physical
activity in the last three years. The Internet is an enormous canvas for people to post everything that they
see in their daily lives. Social media is frequently used for analysis, judgment, inspiration, or emotion
detection. The objective of this work is to analyze sentiment from social media data and detect stress
of various class of people from their social networking platforms. The proposed work is having two
different components: first extraction of information using natural language processing and another is
stress detection using machine learning techniques. Proposed work has 4 main phases: collection of
data from social media, auto summarization, text mining, and stress detection. The proposed model can
predict stress or cognitive load of an online user. The current model has used various machine learning
techniques among them Support Vector Machine is giving good results compared to others techniques,
it has 90% accuracy, 90% recall, 94% precision and F1 score is near about 92%. The current model will
have a positive impact on society for the early detection of stress.
Keywords
Stress Detection, Natural Language Processing, Machine Learning, ICON 2022, WNLPe-Health 2022
1. Introduction
Stress is a normal feeling which can experience by anyone irrespective age group, gender or
community. Most important thing is that how we are reacting with stress that will matter
a lot [1]. The stress can be negative or positive. Especially COVID-19 pandemic affected
almost everyone into stressed phase due to several restrictions like very long duration of
social distancing, lockdown, etc. Due to those restriction people spend a lot of time on social
media during pandemic period [2, 3]. These media is applicable almost in every discipline, to
name some; we’ve, balloting mechanism for splendor festival, political campaigns, product
research and promoting via advertisements. There is need for analyzing and modeling of such
networks. Technology place is growing at a completely fast tempo leading to formation of new
19th International Conference on Natural Language Processing (ICON 2022): WNLPe-Health 2022, December 15–18,
2022, IIIT Delhi, India
Envelope-Open kirti@iiitranchi.ac.in (K. Kumari); simadascse@gmail.com (S. Das)
GLOBE https://iiitranchi.ac.in/ (K. Kumari)
Orcid 0000-0003-3714-7607 (K. Kumari)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
sophisticated gear from textual contents. Data mining techniques are required for his or her
functionality of managing the three dominant residences with social network facts particularly;
length, noise and dynamism. This huge quantity of information of social community requires
automation for dispensation of data, reading it within a stipulated time. Interestingly, statistics
mining techniques are designed to handle the voluminous statistics sets to mine extensive styles
from statistics; social network sites provide these massive records sets due to their utilization
and consequently are best applicants to mine information using the statistics mining equipment.
Therefore, we will infer that the statistics mining or to be specific web mining provides the
vital intelligence to the social network to create and engage in a more humanly and person
friendly manner. Automatic stress detection is emerging as an appealing research area due to
the growing demands for communication between intelligent systems and humans.
Researchers have designed a variety of techniques for analyzing physiological data obtained
from sensors attached to human bodies in order to identify stress and categorize emotions
[4, 5]. But there is very few works which focused on analyzing social media posts for analyzing
the stress of online users. Here, we have tried to analyze the online user behavior styles on
big-scale social networks or even use such social records for further studies. With the help of
Machine Learning algorithms our system will provide accurate and reliable outcomes towards
early detection of stress faced by the online users.
In this study, we propose a system for detecting psychological stress based on online textual
comments. The idea of this work is to come up with natural language processing with machine
learning that not only detects stress but also analyses the topic of debate in a specific social
media text for further mental illness. Along with sentiment analysis, this mechanism will be
useful in examining and segregating the consumer’s opinions on other special subjects. After
wearing out in-depth studies on pertinent datasets we are able to gain vital understandings of
various correlations between social interactions and the anxiety or pressure of the online user.
Contribution of the paper is having following key points:
• Natural language processing techniques used to extract information from the comment.
• Analyze and segregate the person’s reviews on specific topics.
• Stress detection using different types of machine learning methods and also choose an
appropriate method for detection stress with the good accuracy.
The rest of the paper is organized into five different sections. Section 2 for literature survey on
similar task and similar techniques, Section 3 discusses the proposed work and Section 4 for
results and discussion. Finally, we concluded the paper with discussing about future scope in
the Section 5.
2. Literature Survey
Social media has grown significantly during the last ten years, both positively and negatively.
People may now contact each other directly across any cultural or economic divide because
of the rapid growth of networking through social media and the internet. Social media has
numerous advantages [6, 7], but it also has drawbacks that have a bad impact on society.
The active areas of research community are detection of hate, offensive, aggressive and stress
related comments. Stress detection is not a new area of research, there are numerous research
done in the field [8, 9, 4, 5, 10, 11, 7]. Hate speech and Online Aggression are issues that have
emerged during the past few years [12, 13, 14, 15]. The use of derogatory and abusive remarks
on social media is essentially considered hate speech. It could be referring to a single person
or a certain group of people who share the same interests. In this essay, we have outlined
our strategy for combating hate speech and significantly reducing it. People express their
wrath and rage on social media immediately, which is hurtful to other people’s feelings. It
would be extremely detrimental to them and harm their caste, creed, religion, and race. Despite
not necessarily intending to offend anyone, some comments could be considered hate speech
because of the profanity they include. To eradicate hate speech, authors [16] have dig deeply
into natural language processing and used a variety of machine learning models to determine
which one to deploy based on its accuracy.
Our daily lives involve the linkage and retrieval of information. Databases are the most
widely used source of information online. Data volume is increasing quickly, and database
technology is advancing and having a significant impact. Virtually all web apps use databases
to store and retrieve data. The objective is to offer a more user-friendly mechanism for creating
database queries and delivering results. It is feasible to communicate with a large segment of
the people using social media. They [17] increased public access to the Atmospheric Radiation
Measurement Data Center (ADC) data using this medium and Natural Language Processing
(NLP).
The main contributor to disability and a major factor in suicide is depression. In recent years,
social media has become one of the most popular ways to share information online. Most
people communicate their thoughts, philosophies, and personal experiences on the Internet.
The language people use on social media shows that depression has an impact on how they
communicate. Because of the enormous rise in mental health awareness, it is now very important
to recognize mental disease. In the study [18], authors looked at Twitter users’ tweets for any
signs of depression. They employed text mining and natural language processing strategies to
do this. With the CNN and LSTM classification method, they were able to achieve an accuracy
of 92%. Additionally, they evaluated their model against classifiers for logistic regression and
TF-IDF. Here, we have summarised some of the important recent works [19, 20, 21, 22, 23, 24, 9,
25, 26] done in the area of stress detection with their consideration of contents, techniques and
task with comparison of our current system in the Table 1.
It has been extensively studied how to identify stress using physiological signs. In almost all
previous methods, physiological signals from sensors for the electrocardiogram, electrodermal
activity, and electromyography were examined. These methods analysed physiological signals
using conventional machine learning algorithms to detect stress and categorise emotions. In
the current work, we have utilize the social media posts for the detection of stress, discussed in
subsequent section.
Table 1
Comparison table for recently published paper with proposed system based on Stress detection
Source Measurement Techniques Task
Subhani et al. T test, Distance Logistic regression, Support Mental stress detec-
[19] vector machine, Naïve bayes tion
Elzeiny and Electroencephalogram, Hibert- K-Nearest Neighbor Workplace stress de-
Qaraqe [20] Huanf Transform tection
Papini et al. [21] Medical and demographic fea- Logistic regression Posttraumatic stress
tures detection
Jadhav et al. [22] Textual data, facial expression Bidirectional Long Short- Text based stress de-
Term Memory tection from social
media.
Dubey et al. [23] Assisted reproductive technol- Support vector machine Human spermatozoa
ogy detection under ox-
idative stress
Jebelli et al. [24] Electroencephalogram Online Multi-Task Learning Stress recognition
(OMTL) algorithms framework
Das et al. [9] Electroencephalogram Backpropagation Neural Cognitive load detec-
Network tion
Zhang et al. [25] Magnetoencephalography, Support vector machine Posttraumatic stress
Electoencephalogram detection
Yousefi et al. [26] Pupildiameter, electrodermal Linear discriminant analysis Stress detection
activity using eye tracking
dataset
Our proposed sys- Textual data Natural language process- Text based stress de-
tem ing with Support vector ma- tection from Twitter.
chine.
3. Methodology
In this section, we have discussed the dataset collection and annotation in the first subsection.
Then, in second subsection, we discussed the proposed approach.
3.1. Dataset Collection and Labelling
We have collected our dataset by using Tweepy API from the Twitter, which is most frequently
used social media by the online user. For the initial filtration, we have applied words based
identification of Tweets and selected some of the users which are more vulnerable for the stress.
Then, we collected their tweets from last three months and taken for our case study. We have
found 58 users and their total 2978 tweets are selected for our study. Here, we have shown the
simple examples of stressed people reaction, which can be seen in Figure 1. Similarly, we have
shown in Figure 2, some of the important examples of stress related Tweets from our dataset.
We can observe from the Figures 1 and 2, physical stress is more explicitly can be seen but stress
of online users are more implicit, which making the task challenging.
Then, we have manually labelled the tweets into low stressed verses high stressed by the help
of five undergraduate and two postgraduate students. All seven students labelled the tweets
Figure 1: Examples of physical images of Stress
independently. Finally, we have considered the ultimate label with the help majority voting
strategy. The detail distribution of different stress class data shown in Figure 3.
3.2. Proposed Approach
In this section, we have presented our approach towards stress detection through natural
language processing and machine learning techniques.
Unstructured data, which makes up almost 80% of all data in the world, is analyzed and
processed in one of the most crucial ways possible through text mining. Most organizations
and institutions now collect and store enormous volumes of data in data warehouses and cloud
platforms, and as fresh data floods in from various sources, this data continues to expand
dramatically by the minute. Overall, our proposed work is shown in Figure 4, which is designed
with natural language processing with machine learning techniques used to detect stress over
social media extracted information. The proposed system has 4 main phases, first one for
dataset collection from social media, 2nd phase designed for auto summarization of all the post
collected from one specific user shown in Figure 5, 3rd phase designed for text mining which is
shown in Figure 6 and last phase is designed to detect stress that phase shown in Figure 7.
Figure 2: Examples of Stress related Tweets
Figure 3: Distribution of stress data
Collection of Raw Auto Summarization
Data from Social of Textual Text Mining Stress Detection
Media comments (Figure 3) (Figure 5)
(Figure 4) (Figure 2)
Figure 4: Overview of our proposed work
Data Data
Row Comments Data Realization Summary
Analysis Transformation
Figure 5: Auto Summarization of Comments
3.3. Automatic Summarization
Automatic summarization as shown in Figure 5 is the act of computationally condensing a set
of data to produce a subset (a summary) that captures the key ideas or information within the
original text. Here, we have taken all the comments from the same online user and summarized
the comments. The main idea of summarising the comments is that every comment is not equal
important for the stress detection. The approach presented uses K -Means clustering to create
extracts from the parent text. The number of clusters depend upon the size of the input text.
In broader view though, too less number of clusters are unsuitable as they may change the
meaning of the parent text entirely. Similarly large number of clusters would mean that the
size of the extracted text is large which contradicts the purpose of summarization.
3.4. Text Mining
For extracting information from the textual comments, we have utilized the text mining tech-
niques shown in Figure 6. At first we have tokenized the corpus. Then, we used stemming
and lemmatization methods for better understanding of root word or token as normalization
technique. Further, we have used entity recognition for the important terms related to stress
and taken as a separate feature. The most common data pre-processing activity is named entity
recognition (NER). It entails locating important information in the text and classifying it into a
number of predetermined categories. A constant subject of discussion or reference in a book is
referred to as an entity. These extracted information given to the machine learning classifier
for differentiating the low stress verses high stress comments.
Natural language processing (NLP) is an artificial intelligence technique that turns available
(unstructured) text found in documents and databases into normalized, structured data that can
be used for analysis or as input for machine learning algorithms. Figure 6 shows the proposed
model for text mining. Unstructured data, which makes almost 80% of all data in the world, is
analyzed and processed in one of the most crucial ways possible through text mining.
Finally, we have come up with extracted data and with their two labels: Low Stressed and
High Stressed. With these information we have experimented with different classifiers. Here, we
have taken 80% samples for the training and rest 20% for the testing. We have experimented with
two different vectoriser techniques: Count-Vectoriser and Term Frequency-Inverse Document
Frequency (TF-IDF) vectoriser for making suitable input for our classifier. Latter, we found
that TF-IDF is performed well so we left the Count-Vector for latter stage. We have used NLTK
library1 and Scikit-learn2 package for the implementation.
1
https://www.nltk.org/
2
https://scikit-learn.org/
Summarized Lexical Analysis Normalized Entity
Comments (Tokenization) Document Identification
Important Information
Extracted Information Extracted
Stored
Figure 6: Proposed model for Text Mining
Labelling
Extractacted High Stressed
information from the
Tweets Classification
Low Stressed
Feature Selection and
Extraction
Figure 7: Proposed framework for Stress Detection
4. Result and Discussion
In this section, we presented our findings and analysis of experimentation with highlighting
the limitations. In order to avoid a person from experiencing numerous stress-related health
issues, this research provides automated system for stress detection on persons utilizing social
media posts gathered through Twitter API and applying Natural Language Processing and using
various machine learning algorithms. We have also tried basic prepossessing like removing stop-
words, removing URLs, lowercasing the uppercase, etc. Specially, we have experimented with
five machine learning classifiers: Support Vector Machine (SVM), Logistic Regression, Naive
Bayes, Decision Tree and Random Forest classifiers. We have used Term Frequency-Inverse
Document Frequency (TF-IDF) vectoriser for making input to the classifier. For the evaluation
of our system, we have chosen the Accuracy, Precision, Recall and F1-Score as performance
matrices. The Table 2 shows the performance of different classifiers and found that Support
Vector Machine and Random Forest classifiers are performing better than other classifiers in
our case of experimentation.
Table 2
Performance of Proposed Framework
Classifier Accuracy Precision Recall F1-Score
Support Vector Machine 0.90 0.94 0.90 0.92
Logistic Regression 0.89 0.92 0.88 0.89
Naïve Bayes 0.82 0.86 0.83 0.84
Decision Tree 0.78 0.81 0.76 0.79
Random Forest 0.91 0.93 0.87 0.90
We have not tried the deep learning models due to the insufficiency of samples. We have
also not-considered the the tweets which has other than English word and having different
modalities of data while choosing the tweets for the dataset.
5. Conclusion and Future Work
Most people have to deal with stress on a regular basis. However, long-term stress, or a high
level of stress, will jeopardize our safety and disrupt our usual lives. Identifying mental stress
proactively can help to prevent many health problems related with stress. In order to identify
stress more effectively, this study attempts to offer a method for analyzing the mental stage
during stress, based on social media posts made under stressful circumstances. With the help of
the proposed method it is possible to identify people that have anxiety and depressive illnesses
by utilizing prediction models to identify user language on social media, which has the potential
to supplement conventional screening. Predictive models based on machine learning technique
may provide the possibility to diagnose symptoms sooner, perhaps before psycho-social effects
become serious.
In this work, we have experimented with very small volume of data but can be extended for
large volume data for better understanding of stress of online users. Another future scope of
our work is that it can be experimented with multi-lingual posts and multi-modal posts like
image, Meme, audio and video; which is very common in India and also upgraded with features
of social media.
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