Sentimental Analysis Using Machine Learning for Mental Healthcare Management System

Abstract

Mental health issues are becoming more and more common around the world, with more and more people suffering from stress, anxiety, depression, and other related disorders. It has led to the development of innovative approaches to identify and treat the problem. Sentiment analysis, is a computer technique that analyzes text and identifies the emotions behind it, detects and monitors mental health problems in real time. It can be used for analyzing social media posts, chat messages, and other forms of online communication, sentiment analysis can provide valuable insight into the emotional state of individuals and groups. This technology helps mental health professionals identify those in need of support and provide timely interventions, ultimately improving mental health outcomes for individuals and society as a whole.

Introduction

I. INTRODUCTION

In recent years, mental health problems have become more serious worldwide. According to the World Health Organization (WHO), one in every four people in the world will be affected by a mental or neurological disorder at some point in their lives. Mental health problems can manifest themselves in many ways, including depression, anxiety, bipolar disorder, schizophrenia, and addiction. These conditions can have a significant impact on a person's well-being and affect their ability to work, learn and enjoy life. Despite the increasing prevalence of mental health problems, many people do not seek treatment because of the stigma associated with mental illness. Reason being having limited resources or trained professionals, while some lack access to adequate mental health services. This has created a huge gap in mental health care, necessitating innovative solutions to help solve the problem.

One such solution is the use of machine learning and sentiment analysis in mental health management systems. These systems can be used to identify and monitor individuals at risk of developing mental health problems to enable timely intervention and improve mental health outcomes.

Machine learning is a subset of artificial intelligence that involves training computer algorithms to learn from data and make predictions or decisions based on that learning. By analyzing large data sets, machine learning algorithms can find patterns and trends that are not immediately apparent to human observers. This ability makes machine learning suitable for identifying risk factors for mental health problems and predicting who is at risk of developing these disorders.

Sentiment analysis, also known as opinion mining, is a form of machine learning that analyzes text data to identify the sentiment behind it. Using this technique, social media posts, chat messages, and other forms of online communication can be analysed to gain valuable insight into the emotional state of individuals and groups.

Mental health management systems that use machine learning and sentiment analysis can be designed to monitor social media platforms, chat rooms, and other online spaces for signs of mental health problems. Algorithms can be used to identify patterns of language usage such as: changes in tone of voice or use of certain words or phrases that may indicate psychological distress.

The data collected by these systems can be analysed in real time, allowing immediate intervention by mental health professionals. For example, if a person posts a suicide message, the system alerts a mental health professional, who can contact the person and offer support. Mental health management systems can also be designed to provide personalized support to those at risk for mental health problems. By analyzing an individual's online behaviour, these systems can identify factors that may contribute to emotional distress and provide tailored interventions. For example, if a person spends too much time on social media, the system may suggest leaving those platforms and engaging in another activity that promotes mental health.

The benefits of using machine learning and sentiment analysis in mental health management systems are numerous. These systems help identify people at risk of developing mental health problems, improve access to mental health services, and provide personalized support to those in need. Additionally, by monitoring online spaces for signs of emotional distress, mental health professionals can intervene early and potentially prevent more serious mental health problems from developing.

A. Different Levels of Sentimental Analysis

1. Document Layer: This layer analyzes the entire document and classifies sentiment using supervised and unsupervised machine learning techniques. However, at this level it is not possible to compare multiple topics or documents.
2. Sentence Level: This level is used to express emotions. The positive, negative, or neutral from a given sentence. This is closely related to subjective classification, where classifiers from document-level analysis are used.
3. Aspect Level: This level detects the sentiment of a particular aspect of a particular entity. Driving behaviour or vehicle weight. Sentences containing competitive statements are part of this analysis.
4. Phrase Level: This level classifies opinion words contained in phrases. It can provide an accurate opinion about an entity, but the results may not be accurate due to contextual polarity.
5. Feature Level: At this level, we identify features or attributes of the product and analyze opinions about them. Extract positive, negative, or neutral opinions from these features during document analysis.

B. Different Applications of Sentimental Analysis

Here are five uses of sentiment analysis:

1. Social Media Monitoring: Sentiment analysis can be used to monitor and analyze social media platforms to gain insight into customer opinions and preferences for products, services, or brands. This helps companies identify areas for improvement and formulate effective marketing strategies.
2. Customer Feedback Analysis: You can collect customer feedback using sentiment analysis. To analyze reviews and surveys to gain insight into customer satisfaction and identify potential improvements to the products or services offered.
3. Market Research: Sentiment analysis can be used in market research to understand customer sentiment and preferences for various products and services. It enables companies to create new products, improve existing ones and gain a competitive advantage.
4. Brand Reputation Management: Using sentiment analysis, you can monitor and manage your brand's online reputation by tracking customer sentiment towards your brand on social media platforms and other online channels. This allows businesses to identify negative sentiment and take corrective action to improve their reputation.
5. Political Analysis: Sentiment analysis can be used to analyze public opinion on political parties, candidates, and policies. This helps political parties understand public opinion and adjust their strategies accordingly. It can also be used by political analysts and pollsters to predict election results.

C. Advantages of Sentimental Analysis

Here are five distinct benefits of sentiment analysis.

1. Save Time and Resources: Sentiment analysis can process large amounts of data quickly and automatically, saving time and resources compared to manual analysis.
2. Improved Customer Satisfaction: By analyzing customer feedback and sentiment, businesses can identify areas for improvement and take corrective action to improve customer satisfaction.
3. Providing Competitive Advantage: Sentiment analysis provides companies with valuable insight into customer preferences and sentiment toward competitors, providing a competitive advantage.
4. Improved Marketing Strategies: Sentiment analysis helps companies develop more effective marketing strategies by understanding how customers feel about their products, services, and brands.
5. Predictive Analytics: Sentiment analysis can be used for predictive analytics, allowing businesses to anticipate customer needs, trends and preferences and adjust strategies accordingly.

In summary, mental health issues are on the rise and innovative solutions are needed to address this growing concern. A mental health management system using machine learning and sentiment analysis can help identify individuals at risk of developing mental health problems, provide personalized support, and improve access to mental health services. With continued development and implementation, these systems have the potential to significantly improve mental health outcomes for individuals and society as a whole.

II. LITERATURE SURVEY

Mental health problems are becoming more and more common around the world, and the negative effects that come with it have increased the interest in finding effective solutions. One promising approach is sentiment analysis for addressing mental health issues. Sentiment analysis is a method which is used to identify, extract, and quantify emotional states and opinions from different sources, including social media and other forms of digital communication. By analyzing the moods of people's speech, researchers can gain insight into how people think about specific mental health issues and develop strategies for dealing with them.

The World Health Organization reports that more than 450 million people worldwide suffer from some form of mental illness, and this number is expected to continue to grow. The COVID-19 pandemic has exacerbated the situation as many people are experiencing increased stress, anxiety and depression as a result of social isolation, economic insecurity and fear of spreading the virus. I'm here. Therefore, innovative and effective strategies are needed to address the increasing mental health challenges.

Sentiment analysis is a promising solution for addressing mental health issues because it allows researchers to gain insight into an individual's feelings and opinions regarding various mental health issues. Social media platforms, blogs, and other online forums are often used as data sources for sentiment analysis. These sources are valuable because they provide access to large and diverse data pools that can be analysed for patterns and trends. Sentiment analysis can also identify people at risk of developing mental health problems and intervene early.

Various studies have shown the effectiveness of mood analysis and machine learning in treating mental health problems. A study by De Choudhury et al. (2013) used his Twitter data to explore the feelings and emotions of depressed patients and predicted the onset of depression with 70% accuracy. A study by Coppersmith et al. (2015) used sentiment analysis to analyze online suicide prevention forums and correctly identified suicidal individuals with 80% accuracy. Identically, Tsugawa et al. (2015) used sentiment analysis and machine learning to identify depressed people by analyzing their tweets, and achieving 70% accuracy. A study by Guntuku et al. (2017) used machine learning to analyze depression-related social media posts and identified depressed patients with 70% accuracy.

Machine learning plays an important role in mood analysis and mental health research. Machine learning algorithms can be used to analyze large data sets and identify patterns and trends that are difficult to identify using traditional statistical techniques. These algorithms can also be used to identify individuals at risk of developing mental health problems and develop predictive models for early intervention.

In summary, mental health problems are on the rise worldwide, and the negative effects of these disorders have increased interest in finding effective solutions. is one of the promising solutions in By using social media and other digital communication platforms as data sources for sentiment analysis, researchers can gain insight into individuals' feelings and opinions about various mental health issues. Several studies have shown that mood analysis is effective in identifying individuals at risk of developing mental health problems and providing early intervention.

III. OBJECTIVE

1. Challenges Faced While Developing A Sentiment Analysis Website Where Users Can Answer Quizzes To Determine Their Emotional State: Developing a sentiment analysis website that accurately predicts a user's emotional state is a difficult task with multiple complications, such as designing effective quizzes, developing accurate sentiment analysis algorithms, and ensuring privacy and security. Some of the questions explored in this research paper include:

a. Development of quizzes that can accurately capture the user's emotional state.

b. Design sentiment analysis algorithms that accurately analyze user reactions and make accurate decisions.

c. Ensure data protection and security by implementing effective data protection measures.

d. Addressing ethical concerns regarding the use of personal information.

2. To investigate the need for medical clearance and accuracy to ensure the safety and effectiveness of the Website in treating users' mental health problems: A sentiment analysis website that can determine a user's emotional state can have a significant impact on a user's mental health. Therefore, it is important to ensure that websites are medically approved, accurate, and effective in addressing users' mental health issues. This study aims to examine the following aspects:

a. The importance of medical clearance in ensuring the safety and effectiveness of sites that address user mental health issues.

b. Accurate sentiment analysis is required to provide accurate judgments to users.

c. Use of validated psychological measures and tools to ensure website accuracy and effectiveness.

d. Address legal and regulatory issues related to medical licensing and privacy.

3. Evaluate The Importance Of A User-Friendly UI/UX In Improving User Engagement And The Overall User Experience Of Your Website: The success of a sentiment analysis website depends heavily on the user experience it provides. A user-friendly UI/UX significantly increases user engagement and satisfaction, leading to improved mental health. This study aims to examine the following aspects:

a. The importance of a user-centered approach when designing sentiment analysis websites.

b. The need for a simple, intuitive and visually appealing UI/UX to improve user engagement.

c. Use gamification and feedback mechanisms to increase user engagement and engagement.

d. Assess user satisfaction and engagement with the site through user testing and feedback.

4. To Provide Users With Effective Recommendations Based On Sentiment Analysis: A sentiment analysis website that accurately predicts a user's emotional state can provide users with valuable insight into their mental health. To make your website more effective, it is important to provide users with effective recommendations based on the results of sentiment analysis. This study aims to examine the following aspects:

a. Use personalized recommendations based on sentiment analysis results.

b. The importance of evidence-based recommendations in addressing user mental health issues.

c. Using behavioural activation techniques and cognitive-behavioural therapy in making recommendations.

d. Address ethical concerns related to providing recommendations based on personal data.

5. To Enable Users To Access Professional Help And Resources For Dealing With Mental Health Issues: Sentiment analysis websites can provide users with a valuable initial assessment of their mental health. However, it is important to provide users with access to professional help and resources to effectively deal with mental health issues. This study aims to examine the following aspects:

a. The importance of providing users with access to professional help and resources.

b. Use referrals and telemedicine services to connect users with mental health professionals.

c. Complement professional support with online mental health resources and self-help tools.

d. Address ethical concerns related to providing professional assistance and resources.

IV. METHODOLOGY

This research paper examines the use of sentiment analysis technology to help people identify and overcome mental illness. The project goals were determined by extensive research and development to overcome various challenges. The result is a platform that offers users his two methods for checking their mental and emotional state. The first option is Quick Analysis, which asks the user a series of multiple-choice questions. Users are given emotional state ratings and scores. The second option is full-blown sentimental analysis, which requires capturing a user's face using a camera feature that uses facial recognition technology. After answering a series of questions, users receive an emotional state assessment, a score, and a set of recommendations, including songs, meditation tasks, and information about mental health doctors. Our website offers a quick analysis feature that allows users to answer a series of multiple-choice questions. The questions are designed to collect information about the user's emotional state, and the answers are analysed to determine their mental or emotional health. This analysis is based on sophisticated techniques that identify specific patterns of user reactions to determine their emotional state. Now before we discuss the two features in detail let’s take look at the user sign-up or login page, dashboard and Helper page.

A. Sign-up & Login

Users can sign up for her platform online by providing basic information such as name, email and password. After successful registration, users can log in using their access data. For password resets, the platform will send a reset link to your email address.

Conclusion

In conclusion, mental health problems affect a significant proportion of the world\'s population, but the associated stigma and lack of access to mental health services often prevent people from seeking help. This technology offers a promising solution to address these challenges. Machine learning and sentiment analysis are used in mental health management systems to analyze data from social media platforms and other online spaces to identify individuals at risk of developing mental health problems and provide personalized and timely can provide support and provide intervention. In this context, a platform based on sentiment analysis technology offers her two user-friendly options for monitoring mental and emotional health. The Quick Analysis feature provides a quick and easy way to check your emotional state and get ratings and recommendations. Sentiment analysis options go even further by using facial recognition technology to understand the user\'s health more holistically and provide more personalized recommendations. The site also has a helper page that allows users to seek professional medical assistance and access resources to help them better understand and manage their mental health. Overall, the platform aims to provide a safe space for individuals to seek help and support for their mental health issues and improve their quality of life through proactive interventions. Mental Health Professionals Homes have the opportunity to use these tools to improve mental health outcomes and promote the overall well-being of those in need.

References

[1] Department of Information and Technology, G.H. Raisoni College of Engineering, Nagpur, India; TITLE OF PAPER: Mental Healthcare Management And Development System With Machine Learning has been published in IJARESM, Impact Factor: 7.429, Volume 11 Issue 3, March- 2023 Paper Id: IJ-2401231204 Certificate No.: 323231012 Date: 31-03-2023 [2] “Application of Machine Learning Methods in Mental Health Detection: A Systematic Review”; Rohizah Abd Rahman, Khairuddin Omar, Shahrul Azman Mohd Noah, Mohd Shahrul Nizam Mohd Danuri, Mohammed Ali Al-Garadi [3] “A Machine Learning Implementation for Mental Health Care. Application: Smart Watch for Depression Detection”; Piyush Kumar, Rishi Chauhan, Thompson Stephan, Achyut Shankar, Sanjeev Thakur [4] “Mental Wellness: Understanding Expert Perspectives and Student Eperiences” Chirstina Kelley, Bongshin Lee and Lauren Wilcox [5] “Mental Stress Level Prediction and Classification based on Machine Learning”; Akshada Kene, Shubhada ThakareI. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange aelotropic,” in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1964, pp. 270–340. [6] Avasthi A, , Ghosh A, , Sarkar S, et al. Ethics in medical research: Ordinary principles with particular reference to psychiatric research. Indian J Psychiatry, 2014; 55 86–91. [7] Deborah Oyine Aluh, Olaniyi Ayilara, Justus Uchenna Onu, Ugn? Grigait?, Barbara Pedrosa, Margarida Santos-Dias, Graça Cardoso and José Miguel Caldas-de-Almeida Citation:International Journal of Mental Health Systems 2022 16:54 [8] Grover S, , Gupta BM, , and Dhawan SM. Schizophrenia research in India: A scientific review of Indian publications from 1990 to 2019. Asian J Psychiatry, 2021; 56: 103521. [9] Shah B, , Parhee R, , Kumar N, et al. Mental fitness studies in India. 2006. New Delhi: Division of Noncommunicable Diseases, Indian Council of Medical Research.

Copyright

Copyright © 2023 Prof. Priti Kakde, Aditya Armarkar, Aman Kathale, Aryant Kshirsagar, Akshay Lokhande, Aditya Taitkar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.