KEC _ AI _ MIRACLE _ MAKERS @ LT - EDI -2024: Stress Identification in D ravidian Languages using Machine Learning Techniques (2024)

Abstract: Identifying an individual where he/she is stressed or not stressed is our shared task topic. we have used several machine learning models for identifying the stress. This paper presents our system submission for the task 1 and 2 for both Tamil and Telugu dataset, focusing on us- ing supervised approaches. For Tamil dataset, we got highest accuracy for the Support Vector Machine model with f1-score of 0.98 and for Telugu dataset, we got highest accuracy for Random Forest algorithm with f1-score of 0.99. By using this model, Stress Identification System will be helpful for an individual to improve their mental health in optimistic manner.

## introduction stress, anxiety, and depression (sad) are psychological disorders that have a serious negative impact on mental stability. these disorders interfere with an individual's ability to go about their everyday life normally and can sometimes worsen into trauma. the human body releases a variety of chemicals when under stress, despair, or worry, and this results in alterations to nonverbal body language. these psychological diseases can be generically categorised as stress, anxiety, and depression according to the different stages involved in their exploration. stress is the second stage of mental illness, during which psychological illnesses become more moderate since anxiety is a persistent factor. the third most serious psychological condition that can have a long-term negative impact on a person's physical and mental health is depression.an individual's level of discomfort is a result of stress, and this discomfort manifests as anxiety or depressive episodes. stress is the culmination of all the things that can make someone feel stressed out. exercises, additional work, a task overload, shallow breathing, insufficient sleep, questionnaires, etc. are examples of stressors. according to a study, stress can have a beneficial or negative effect depending on the circumstances. the study looked at people's social media posts, where they shared their feelings and emotions, to determine whether or not they were stressed. social media posts in code-mixed tamil and telugu should be classified as either stressed or not stressed by the system. numerous machine learning methods, including the random forest, naive bayes, and support vector machine (svc) algorithms, have been employed. this is how the rest of the paper is structured. the literature on work linked to stress identification is briefly discussed in section 2. section 3 provides a detailed description of our system, and section 4 presents the findings and conclusions from the experiments. we wrap off the work by discussing potential implications for further research. ## literature review in @xcite , the researchers have used some existing computer vision models for systematic review and used machine learning algorithms to detect sad, which is more efficient than medical investigations because machine learning is fast and best for computing stress. the proceedings in @xcite ,the researchers have used surface electromyography signals (semg) for detecting stress with the help of convolutional neural networks, and they got moderate range of the macro f1-score for a bi-class and multi-class classification. but they didn't provide necessary information about the size or diversity of the dataset used for training and testing, and insights into the interpretability of the model. s and karthick (2022) have also used the deep learning modal with a convolutional-based network approach and multimodel data with the help of sensors in which the data are collected, such as heartbeat, body temperature, respiration, electromyographic (emg) data, and additional long and shortterm memory is used. they didn't provide any limitations or drawbacks. in tahira and vyas (2023) a hybrid deep learning model that combines bidirectional long shortterm memory (blstm) and convolutional neural networks (cnn) is presented for exploiting eeg signals to determine stress. even though the modality got higher accuracy, they didn't explore other potential factors that are responsible for the stress. in @xcite , the researchers used the bert model for text-based research and achieved better range of the f1-score, and they combined stacked transformer encoder layers with stacked bi-directional lstm. but it did not explore other modalities such as signal-or speech-based analysis, and it is not clear how the model's performance compares to other existing state-of-the-art models in stress analysis. in suba raja et al. (2023), they will send the test data through sms alerts using a gsm module by extracting facial expression and mapped onto the emotion space and the eeg signal value is evaluated. the accuracy and robustness have been limited for the evaluation of this system and have not been discussed the potential limitations. saputra and nafi'iyah (2022) used feature extraction techniques including mean, standard deviation, and mav, were applied to the eeg signals to capture relevant information. they have used several machine learning models to features, but the knn algorithm achieved the highest accuracy in distinguishing between stressed and normal individuals. but they did not provide information about demographic characteristics and also not investigate the impact of external factors. @xcite aimed to identify the stress among individuals using machine learning and wearable sensors with a random forest model in both binary and three-class classifications, achieving macro f1-scores of 83.34 and 65.73, respectively.but this paper fails to discuss the ethical considerations and privacy concerns related to the use of the wearable sensors. @xcite provides a comprehensive review and analysis of supervised learning (sl) and soft computing (sc) techniques used in diagnosis and the potential use of the hybrid technique gives a more accurate stress diagnosis. their limitations are due to the factors such as real-time data collection, bias, integrity, multi-dimensional data, and data privacy. kul @xcite focuses on predicting and detecting stress in individuals by using iot technology and body sensors, and that uses deep learning algorithms to analyze this data and suggest sending alerts, messages to the individual's relatives for support. but they didn't compare with any other existing methods and didn't provide any real-world validations of the proposed modal in practical scenarios. ## problem and system description from the given dataset, we have to train the model whether the given sentence is stressed or not stressed. this shared task is to detect the individuals whether he/she affected by stress from their social media postings by analysing their shared feelings and emotions. given dataset of social media postings consists of both tamil and telugu languages with this, we have to classify the given test data with 2 labels namely "stressed" or "not stressed". ## experimental analysis in this experiment we have used 2 different languages of dataset and 3 machine learning model to predict the class label whether it is "stressed" or "non-stressed". in tamil dataset, we have gotten accuracy of 98.09% in svm classifier,97.27% in random forest algorithm and 89.19% in naïve bayes algorithm. as of our accuracy result, all the model will have the high accuracy hence we considered the support vector machine algorithm as the best algorithm among the other algorithm and it also have 0.98 macro f1-score. in telugu dataset, we have got accuracy of 98.9% in svm classifier,99.01% in random forest algorithm and 92.9% in naïve bayes algorithm. as of our accuracy result, all the model will have the high accuracy hence we considered the random forest algorithm as the best algorithm among the other algorithm and it have macro 0.99 f1-score. ## conclusion stress identification is a very sensitive topic where many people around us and we also got stressed now-a-days. some peoples are handling the things in practical ways but most of 80% of peoples are going to the depression state and they are pushed to take the wrong decision by the surroundings. hence stress identification system will help an individual to improve their mental health in positive