Depression On Socila Network Using Python Django .pptx

, multiple studies have found a strong link between heavy social media and an increased risk for depression, anxiety, loneliness, self-harm, and even suicidal thoughts. Social media may promote negative experiences such as: Inadequacy about your life or appearance.

1. DEPRESSION ON SOCIAL NETWORK

2. Depression is a serious mental health issue for people world-wide irrelevant of their ages, genders and races. In this age of modern communication and technology, people feel more comfortable sharing their thoughts in social networking sites (SNS) almost every day . The objective of this paper is to propose a data-analytic based model to detect depression of any human being . Depression level of a user has been detected based on his posts in social media . These methods need a huge amount of data from the person.  Natural Language Processing (NLP), classified using Support Vector Machine (SVM) and Naïve Bayes algorithm to detect depression potentially in a more convenient and efficient way. INTRODUCTION

3. INTRODUCTION Traditionally, doctors diagnose depressed people face to face via referring to clinical depression criteria. However, more than 70% of the patients would not consult doctors at early stages of depression, which leads to further deterioration of their conditions. Meanwhile, people are increasingly relying on social media to disclose emotions and sharing their daily lives, thus social media have successfully been leveraged for helping detect physical and mental diseases.

4. RESEARCH METHODOLOGY

5. DESIGN ARCHITECTURE

6. We summarize the main contributions in three aspects We construct benchmark datasets for online depression detection and analysis, including the well- labeled depression and non-depression datasets as well as a largescale depression-candidate dataset. In addition, we release these datasets with features to facilitate wellness study for computer science and psychology. We extract six groups of discriminant depression oriented features to describe users from different aspects. As only few of the users’ behaviors are symptoms of depression, we present a multimodal depressive dictionary learning model to learn the sparse representation of users. Our methods can be used to timely detect depression, take proactive care to prevent the depressed condition to be deteriorated. We analyze feature contributions and online behaviors of depression. We make our efforts to reveal the behaviors not covered in depression criteria, trying to provide more perspectives and insights for depression researches.

7. ALGORITHMS USED Support Vector Machine (SVM) Naïve Bayes Support Vector Machine   Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. Though we say regression problems as well it’s best suited for classification. The main objective of the SVM algorithm is to find the optimal hyper plane in an N-dimensional space that can separate the data points in different classes in the feature space.

8. Types of Support Vector Machine Based on the nature of the decision boundary, Support Vector Machines (SVM) can be divided into two main parts: Linear SVM:  Linear SVMs use a linear decision boundary to separate the data points of different classes. When the data can be precisely linearly separated, linear SVMs are very suitable. This means that a single straight line (in 2D) or a hyperplane (in higher dimensions) can entirely divide the data points into their respective classes. A hyperplane that maximizes the margin between the classes is the decision boundary. Non-Linear SVM:  Non-Linear SVM can be used to classify data when it cannot be separated into two classes by a straight line (in the case of 2D). By using kernel functions, nonlinear SVMs can handle nonlinearly separable data. The original input data is transformed by these kernel functions into a higher-dimensional feature space, where the data points can be linearly separated. A linear SVM is used to locate a nonlinear decision boundary in this modified space. 

9. Advantages of SVM Effective in high-dimensional cases. Its memory is efficient as it uses a subset of training points in the decision function called support vectors. Different kernel functions can be specified for the decision functions and its possible to specify custom kernels.

10. The Naïve Bayes classifier is a supervised machine learning algorithm that is used for classification tasks such as text classification. They use principles of probability to perform classification tasks. Naïve Bayes is part of a family of generative learning algorithms, meaning that it seeks to model the distribution of inputs of a given class or category. Unlike discriminative classifiers, like logistic regression, it does not learn which features are most important to differentiate between classes . This theorem, also known as Bayes’ Rule, allows us to “invert” conditional probabilities.  Naïve Bayes classifiers

11. TECHNOLOGIES USED PYTHON Python is an interpreted, object-oriented, high-level programming language with dynamic semantics developed by Guido van Rossum . It was originally released in 1991. Designed to be easy as well as fun, the name "Python" is a nod to the British comedy group Monty Python . Python is extensively applied in data science, data analysis, machine learning, data engineering, web development, software development, and other fields.

12. DJANGO Django is an open-source framework for backend web applications based on Python — one of the top web development languages. Its main goals are simplicity, flexibility, reliability, and scalability . Django can be (and has been) used to build almost any type of website — from content management systems and wikis, through to social networks and news sites. It can work with any client-side framework, and can deliver content in almost any format (including HTML, RSS feeds, JSON, and XML ). D jango is often considered a full-stack web framework. A full-stack framework provides tools and features for both the front-end (client-side) and back-end (server-side) development of web applications.

13. OPENCV Opencv is a huge open-source library for computer vision, machine learning, and image processing. Now , it plays a major role in real-time operation which is very important in today's systems. By using it, one can process images and videos to identify objects, faces, or even the handwriting of a human . OpenCV's functions in Python are the fundamental building blocks of the library, providing low-level operations for manipulating and processing images and matrices. These functions form the backbone of OpenCV and are used by many of the higher-level functions and algorithms in the library.

14. TENSORFLOW Heavily used by data scientists, software developers, and educators, TensorFlow is an open-source platform for machine learning using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them . TensorFlow has better computational graph visualizations. Which are inherent when compared to other libraries like Torch and Theano . Google backs it and has the advantages of seamless performance, quick updates, and frequent new releases with new features.

15. SCREENSHOT

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