Preliminary identification and therapeutic support of depression in mental health using conversational AI

World Health Organization statistics indicate that one out of every eight people suffers from mental illness. Due to the fear of stigma and social discrimination, they start being resilient and end up going through difficult situations alone. They fear criticism and start isolating them from friends, family and neighbours. The majority of individuals don’t have access to effective care. If the issue isn’t treated with care it can lead to serious mental problems such as it may cause depression, obsessive compulsive disorder, anxious or personality disorder. In order to overcome this problem, our mental health chatbot was created. Our study aims to provide efficient and essential care to the people with mental health concerns according to their needs and supplying the basic information regarding mental health problems through various sources. The proposed system eases the preliminary identification of mental health problem in the user by identifying and providing level-I therapeutical support for depression by employing conversational AI.Thisresearch utilizes technologies like Artificial Intelligence and its subfield Natural Language Processing (NLP) to provide an amicable environment for the user 24/7 and it can be integrated in cross platforms like iOS, android and windows etc. A knowledge base retrieval flow network is created with data which is stored globally through which the data is retrieved at the faster rate. After the user enters the chatbot they can converse with the bot initially else he/she also has the option of taking the assessment directly after entering the bot. Behind this process, sentiment analysis takes place which classifies the text into positive, negative or neutral. Once the score exceeds the range it was initially set then it will give the result accordingly. The dataset used in this study is AFINN-en-165 which already has pretrained list of words with the score. The program employed in the entire system is written through Flutter framework. This system allows the users to schedule appointment, to learn in detail about the terminologies of mental health and it provides resources for feel good activities like videos and music. Through this system they can candidly express their feelings to the conversational AI chatbot besides their insecurity. The Artificial Intelligence (AI) in turn provides them with chat support, acts as a bridge to understand the situations and suggests solutions depending on the level of mental health deterioration. We propose a fully automated and powerful first-level detection and support system for mental health

ANICE : An Artificial Neuro-Linguistic Interactive Computer Entity

Mental health problems are hard to talk about, especially when the questions asked do not allow the individual to answer freely. That is the case for most inquiries, where questions usually request very restricted answers like yes or no. This thesis proposes a chatbot that tries to avoid the problem of restricting users to small answers. The chatbot will focus on people feeling burned out due to stress related to their studies. The chatbot tries to replicate two forms with questions about burnout that are used as guidelines. Both these forms are developed based on questions done by psychologists. Because rule-based chatbots have a limited vocabulary, natural language understand- ing and neural-based techniques are tested and validated to see if the chatbot performs well using these techniques. The techniques tested are word2vec and spacy components. The evaluation results show that it is feasible to implement a chatbot that uses rules and also techniques for natural language processing. Additionally, the tests did indicate that both spacy and word2vec are great resources for NLU. Word2vec proves to perform slightly better at specific times related to identifying intents that are domain-specific. Finally, the results from the users experience show that this is a promising work that could help students dealing with burnout.Problemas de saúde mental são um tema difícil de abordar, especialmente quando as perguntas feitas não permitem ao indivíduo responder livremente. Este é o caso da maio- ria dos inquéritos, onde as perguntas geralmente exigem respostas muito restritas, como sim ou não. Esta tese propõe um chatbot que tenta evitar o problema de restringir os utilizadores a pequenas respostas. O chatbot concentrar-se-á em utilizadores que se sen- tem esgotados devido ao stress relacionado com os estudos. O chatbot tenta replicar dois formulários com perguntas sobre burnout, isto é, estes formulários são utilizados como diretrizes. Ambos os formulários são desenvolvidos com base em perguntas feitas por psicólogos. Como os chatbots baseados em regras têm um vocabulário limitado, a compreensão da linguagem natural e as técnicas baseadas em redes neuronais são testadas e validadas para ver se o chatbot tem um bom funcionamento utilizando essas técnicas. As técnicas baseadas em redes neuronais que são testadas são o word2vec e componentes spacy. Os resultados da avaliação mostram que é viável implementar um chatbot que uti- lize regras e também técnicas de processamento de linguagem natural. Além disso, os testes indicam que tanto os componentes spacy quanto o word2vec são ótimos recursos para processamento de linguagem natural. O Word2vec tem um desempenho um pouco melhor em momentos específicos relacionados à identificação de intenções do domínio de estudo. Por fim, os resultados da experiência dos utilizadores mostram que este é um trabalho promissor que pode ajudar os utilizadores a lidar com o burnout

Computational Persuasion using Chatbots based on Crowdsourced Argument Graphs & Concerns

As computing becomes involved in every sphere of life, so too is persuasion a target for applying computer-based solutions. Conversational agents, also known as chatbots, are versatile tools that have the potential of being used as agents in dialogical argumentation systems where the chatbot acts as the persuader and the human agent as the persuadee and thereby offer a costeffective and scalable alternative to in-person consultations To allow the user to type his or her argument in free-text input (as opposed to selecting arguments from a menu) the chatbot needs to be able to (1) “understand” the user’s concern he or she is raising in their argument and (2) give an appropriate counterargument that addresses the user’s concern. In this thesis I describe how to (1) acquire arguments for the construction of the chatbot’s knowledge base with the help of crowdsourcing, (2) how to automatically identify the concerns that arguments address, and (3) how to construct the chatbot’s knowledge base in the form of an argument graph that can be used during persuasive dialogues with users. I evaluated my methods in four case studies that covered several domains (physical activity, meat consumption, UK University Fees and COVID-19 vaccination). In each case study I implemented a chatbot that engaged in argumentative dialogues with participants and measured the participants’ change of stance before and after engaging in a chat with the bot. In all four case studies the chatbot showed statistically significant success persuading people to either consider changing their behaviour or to change their stance

Rexford Guy Tugwell and the New Deal

Thesis (Ph.D.)--Boston UniversityRexford Guy Tugwell, Professor of Economics at Columbia, joined the Roosevelt circle in March, 1932. He was an Assistant Secretary of Agriculture, 1933-34. He helped to write the National Industrial Recovery Act and the Agricultural Adjustment Act. He was an idea man; a publicist ; and an errand boy, bringing academicians, or their ideas, to Roosevelt. He was a member of several inderdepartamental boards. Overestimations of Tugwell's influence rested on the assumption that his intellectual impact on Roosevelt was decisive. Roosevelt used or disregarded Tugwell's ideas as he saw fit. Some policies were in accord with Tugwell's thinking; it is impossible to measure the professor's impact on such matters. Roosevelt took no action on some of Tugwell's ideas, especially those involved in the institutional economist's concept of "conjecture." In one exceptional case, the field of fiscal policy, money, and banking, initial rejection of Tugwell's ideas was followed, to some extent, by thier implementation -- in the "Second" New Deal. Tugwell's impact in this instance was indirect -- he was largely responsible for Marriner S. Eccles' coming to Washington. [TRUNCATED

Bowdoin Orient v.62, no.1-27 (1932-1933)

https://digitalcommons.bowdoin.edu/bowdoinorient-1930s/1002/thumbnail.jp