Mental health diseases analyzer mobile application that empowers users to access and manage their stress levels effectively. Users can register, log in, there will appear three modules , user will select the role (general user, Reseachers,Mental health professional,)and user will select any disease from the dropdown after selecting the disease press on calculate results it will show the results accordingly , For general user Questionnaire screen will appear , researcher will do text analysis on the provided text (Tweets) ,if user give any text or disease to the train model the application shows the results on the basis of trained model, in the third module user will see the setting page and user can share results to the any other mobile application such as whatsaap in the form of link as a report. The ultimate goal is to provide a user-friendly and dynamic platform that enhances mental well-being by combining technology, psychological support, and curated stress-relief.
Objectives
The main goals and objectives of our Android application for Sentiment analysis of social media include: • Sentiment Analysis Accuracy • User-Friendly Interface • Backend API Integration • Scalability and Performance • Customization and Filtering • Insightful Visualizations • Data Security and Privacy: • Offline Functionality • Export and Share Features • Feedback Mechanism • Documentation and Support.We are applying Natural Language Processing and Machine Learning procedures to fabricate a framework that given a bunch of tweets from a twitter users who are tweeting about depression, stress and anxiety issues mainly Mental Health issues. And henceforth in danger User. For this errand, we need to distinguish valuable literary highlights which can achieve by Scikit libraries.we break down the exhibition of various AI calculations, for example, Naïve Bayes (NB), Decision Tree (DT), Support Vector Machine (SVM).
Socio-Economic Benefit
The Machine learning powered health analyzer holds social economics benefits as it is for the well being of mental health of human kind, The application is design to diagnose the mental health level such as anxiety , depression , stress etc and phychotic illness. this application is the powerful tool for cultivating phycological reselience and building metal health by becoming more aware of our thoughts and feelings so that we can identify that either the person is depressed or happy as we have taking data in the form of questionnare on the basis of that it will provide the certein percentage so that we can measure the happiness and depression level,as it is helping in identifying the mental health state ,after knowing the state a person can use it as their primary care doctor to check their mental health spontaneously.
Methodologies
In the system, the primary actor is the "user." The user interacts with the application through
various functionalities, contributing to a comprehensive and user-centric experience. The user can
engage with the system in the following ways:
Questionnaire Submission:
Users can submit two types of questionnaires, contributing to the calculation of happiness and
depression levels based on their responses to multiple-choice questions (MCQs). This feature adds
a personal and introspective dimension to the application.
View Tweets from Backend:
The user has the ability to access and view tweets from the dataset present in the backend,
seamlessly linked with the API. This functionality allows users to stay informed about real-time
sentiments expressed on Twitter.
Keyword Analysis:
Users can perform keyword analysis against the tweets in the backend, leveraging the sentiment
analysis capabilities of the system. The application displays graphical representations and relevant
tweets associated with the specified keyword, enabling users to gauge public sentiment on a
particular topic.
Disease Search:
On the search screen, users can select a disease from a dropdown menu. Upon selecting a disease,
the system performs sentiment analysis on tweets related to that disease, presenting graphical
representations and associated tweets. This functionality aligns with the overarching theme of the
application, focusing on sentiment analysis in the context of health-related topics.
In essence, the user serves as the central figure interacting with the system, engaging in
questionnaire submissions for personal emotional assessment and utilizing diverse features to
explore, analyze, and visualize sentiments expressed in the Twitter dataset linked with the backend
through the API. This formalizes the user's role within the application, emphasizing the versatility
of interactions and the multifaceted nature of the user experience.
User Interface is divided into different screens in our application mentioned below:
- Dashboard (Main Screen for User Interface)
- Update Profile
- Questionnaire
- Analyze
- Happiness Forum
- Depression Forum
- Search Screen
- Tweets
- Share
- Settings
Outcome
The project's desired outcome is a comprehensive mental health diseases analyzer mobile application that empowers users to access and manage their stress levels effectively. Users can register, log in, there will appear three modules , user will select the role (general user, Reseachers,Mental health professional,)and user will select any disease from the dropdown after selecting the disease press on calculate results it will show the results accordingly , For general user Questionnaire screen will appear , researcher will do text analysis on the provided text (Tweets) ,if user give any text or disease to the train model the application shows the results on the basis of trained model, in the third module user will see the setting page and user can share results to the any other mobile application such as whatsaap in the form of link as a report. The ultimate goal is to provide a user-friendly and dynamic platform that enhances mental well-being by combining technology, psychological support, and curated stress-relief resources.
