{'fraction_non_alphanumeric': 0.0170863309352518, 'fraction_numerical': 0.0017985611510791368, 'mean_word_length': 5.149171270718232, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Detecting and Tracking Emotions to Identify People Suffering from Prolonged Mental Stress

In today’s fast paced lifestyle, humans are least interested in themselves. Earlier, they used to share their problems and mental trauma with at least someone, but nowadays, they prefer not to share their hardships with anyone. Due to this introvert nature, they feel crushed and sometimes take catastrophic steps such as suicide. We can leverage the ability of Deep Learning algorithms to bridge this communication gap of the 21st century and identify the individuals from a large group who are experiencing mental trauma for a prolonged period of time. Facial expressions are significant in conveying people’s emotion and state of mind. By tracking these emotions over a significant interval, one can identify, understand and help people who might be undergoing some psycho social issues in life. Therefore, this paper aims at developing a system which accurately recognizes and tracks facial expressions in real time with the help of a Convolutional Neural Network(CNN) and provides us with the details of such people.