After a lot of research, there is no doubt in saying that overall physical, psychological and communal welfare of a human being is predominantly dependent on their mental health. Thus, early recognition and mediation in addressing issues regarding it should be our topmost priority. Individualized and ubiquitous sensing technologies such as smartphones, smartwatches, activity trackers, etc. allow continuous trackingand gathering of data in an undisturbed and low-profilemanner. Sentimental Analysis using Natural Language Processing has been proposed to be applied to the collected data to predict user information such as mood, activity, mental status, depression, anxiety, stress. The intent of this survey is to analyze and propose a methodology for data extraction and a model using Lambda Architecture to study the social media presence and other available data to predict the mental state of the user along with taking additional measures to maintain the secrecy of the user along with dataprivacy. Keywords : Mental Health, Natural Language Processing, Sentiment Analysis, Health Monitoring System, Mental Disorders, Smart Phones, social media, Lambda Architecture.
Emotions are certain complicated neurological expression that constitutes of three sections: a sentimental occurrence, apsychological and physical reaction, and a sociological or revealing reaction. Mental State of a person is the perspective of that person and furthermore gives a sign of his/her general nature. Psychological sickness is a result of lopsided characteristics in cerebrum science. Depression is one of the most well-known and debilitating mental issues which relevantly affects society. Despondency and schizophrenia are the primary purposes behind most suicides because of bad mental health. There are various reasons that can cause depression, one of the reasons is being subjected to pressure. The assessment of mental well- being is not only important but necessary to comprehend and proposetreatments for patients with a strayed mental conduct. These days, a few applications have been proposed for depression and, by and large, for psychological wellness issues. These applications incorporate selfobserving and mean to follow anxiety symptoms and to furnish unwinding activities to assist people with misery, uneasiness issues, outrage the executive’s issues, and so on. They are not intended to supplant customary treatment, for example, Intellectual Behavioral Therapy but can be used to enhance it. Self-destruction avoidance to mental turmoil people would thus be able to be of profiting the general public on the loose. The intricate issue is to recognize the person who has psychological instability. One in each four people are influenced by a psychological issue at some stage of their life. Considering enormous number of individuals that are legitimately and by implication experiencing psychological sickness, it is critical to contemplate the techniques that help in the distinguishing proof of psychological instability, follow and foresee the developing dysfunctional behavior procedures. Estimation and emotional detecting technology could assist with handling these goals by giving successful apparatuses and frameworks for target appraisal. Such devices and frameworks don't expect to supplant the clinician or therapist yet they could bolster their choices.
To provide an estimate of how prevalent the effects of mental illness are in society, consider the following statistics:
According to recent estimates, about 20% of Indians, or about one in five people over the ageof 18, suffer from profound mental disorder in a givenyear Out of 10, 4 of the disabilities are Mental Illnesses such as Bipolar Disorder, Clinical Depression, Obsessive Compulsive Disorder (OCD) and Schizophrenia.
Sentiment Analysis is defined as the recognition of the text to be positive, negative or neutral. Sentiment Analysis is generally implemented using Machine Learning where the input text 'T' is given and a list of emotion types are given based on which the emotion is determined. The generalized pipeline of Sentiment Analysis could be broken down into 5 different steps: • Input text:- The Input text could be in the form of Text, PDF, Audio Input and HTML Files. • Pre-Processing:- Pre-Processing of input data is necessary in order to make the data in a standard format so that the optimized algorithm could process it and provide the most efficient result. Pre- process consist of process of Stemming, Lemmatization, Tokenization, Stop word removal, Removal of repeated characters and even spell check. • Feature Extraction:- The process of Feature Extraction is used in the identification of parts of speech in the in input text. It helps for proper text formation. • Feature Selection:- Feature Selection includes Information gain, selection based on Frequency, Point wise mutual information and gain ratio. • Sentiment Classification:- Sentiment Classification can be achieved using multiple methodology which include Classification, Regression, Clustering and Association. The Lexicon based approach uses a set of words to determine the sentiment of the text. The sentiment is divided into three main types Positive, Negative and Neutral. There are 2 main approaches in the Lexicon based prediction Dictionary based and corpus based approach. The input text after preprocessing is tokenized and then compared with a dictionary set of values which determine the nature of the text, based on this approach the entire sentence is given a value and then its sentiment is calculated. In the corpusbased approach, using the seed list of the words and help from various semantic techniquemorecontextspecific words are identified. It is an iterative process that begins with a defined word collection but, by using multiple sources, broadens its quest range by using alternative synonyms, originating from the seed set of terms of opinions and using numerical and linguistic techniques, certain words of opinions belonging to a specific context are found in known corpus like dictionary and thesaurus.
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