Browsing by Subject "text"

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Computational Methods in Psychotherapy: Prosodic and Semantic Analysis Framework for Clients with Major Depression
(2022-05) Cruz, John Henry; Pennebaker, James
With increased awareness of mental health disorders, the growing practice of psychotherapy was followed by the development of therapy models. These practices have shown themselves to be effective in treating mental health disorders through analysis of different markers seen throughout a therapy session. But still, most of this practice is heavily dependent on decisions made by a therapist, which, to a degree, are subjective. As further developments are seen in computer science, tools have been created with the purpose of analyzing natural language in the form of text and audio. Due to the development of models aimed to understand human language and emotion, we can bridge together text and audio tools along with a well-experienced therapist to develop a therapy analysis computer program. I conducted a literature review to propose a computer program that would focus on the objective features, specifically text and audio, seen in clients in therapy sessions. This could aid therapists in the decision-making process for the treatment of major depression alongside their expertise. Preliminary results include a set of metrics that measures indicators of depression, such as lower speaking frequencies and slower talking speeds. In addition to the literature review, I conducted a needs assessment survey to gauge opinions on potential metrics and the use of the computer program from those currently involved in therapy. Survey results reflected the sentiment that none of the proposed metrics would be useful in reflecting changes in depression while mixed opinions were seen for the computer programs used in assessing depression. Dialogue revolving around the use of technology in psychotherapy could potentially grow in response to the proposed computer program.
Health4Kids: Text Messages
(2022) Latino Research Institute
Communication plan and documentation of the text messages sent to Health4Kids study participants. For more information about text messaging procedures, reference the Health4Kids manual of procedures.
Impasse or Tension - Pedagogy and the Canon Controversy
(1993-09) Bacon, Jacqueline; Bacon, Jacqueline
Towards Computer Generation Of Text
(2019-05-01) Ceverha, Jack; Pennebaker, James
This project explores the challenges of generating theatrical text in the form of a script using automated and semi-automated techniques. Two different systems, a generative system built on neural language models and an original system called Mosaic, were trained on subsets of a corpus of 127 full-length English-language plays. These systems generate short plays using the outline of a handwritten narrative as a source of structure.The language model was used to generate a large amount of plays that were curated and analyzed qualitatively to demonstrate the play’s cohesiveness and the extent to which the generated text matches the handwritten narrative. The system produced suboptimal results in this task -the language model was unable to generate cohesive plays that matched supplied narrativesPlays generated by Mosaic were analyzed qualitatively and through a large experiment that used numerical survey feedback from 300 human respondents on the crowdsourcing platform Amazon Mechanical Turk. Four sets of sixteen-line plays with two characters were compiled using four different protocols. The first set consisted of fifty direct excerpts from the play corpus, while the second set contained fifty plays with lines from the corpus arranged in a random order. These two sets represent the control data. The third set contains fifty plays generated by Mosaic. With the help of ten volunteers, the fourth set contained fifty plays that were written interactively. A volunteer would write a play with Mosaic -Mosaic wrote lines for the first character, and the volunteer wrote lines for the second character.Both experimental sets of plays consistently overperformed the random control to statistically significant degree, with the interactively-writtenplays scoring within five percent of the corpus
When We Fear an Awkward Phone Call, We Often Turn to Text
(2020-09-10) Simon, Jeremy M.