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SNEHA Pullaboina
Resu sai chand goud


: This project mainly concentrates on the comparison of various machine learning algorithms in predicting depression at an early stage. We take a popular reddit dataset as an input and train various models to find the best one among them. Programming language used is python and hybrid algorithms are helpful for detecting the most accurate algorithm that can be used to predict depression using various linguistic metadata features. Depression is a prevalent mental illness characterized by a depressed mood, stressful life experiences, and a sense of despair. It has an impact on your mood and capacity to operate, and it has the potential to lead to suicide. Depression is a substantial contributor to the worldwide burden of mental diseases and is a main cause of disability. According to studies, women are more likely than males to suffer from depression. Around 700,000 people commit suicide each year. Suicide is the fourth leading cause of mortality among people between the ages of 15 and 29. Depression is a common illness that affects 3.8 % of the world's population, with 5.0 % and 5.7 % of people over the age of 60 suffering from it. We target the early diagnosis of sadness in this study by applying several Machine Learning algorithms based on messages and posts on social media networks. Based on word embedding methods WORD2VEC, GLOVE, the Machine Learning algorithm LOGISTIC REGRESSION and the neural network algorithm CONVOLUTIONAL NEURAL NETWORK(CNN) are trained and compared to a classification-based user-level Linguistic metadata


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Pullaboina, S., J.Rachana, Resu sai chand goud, and SATHYAM LAXMI VENKATA PRASAD. “DEPRESSION DETECTION FROM SOCIAL MEDIA DATA USING CNN AND LINGUISTIC METADATA FEATURES”. Technix International Journal for Engineering Research, vol. 9, no. 7, July 2022, pp. 30-35,
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