Semantic Analysis Using Probabilistic Topic Models And Markov Chains

David Zachary Hafner, Alejandro Molina Villegas, Edwyn Javier Aldana Bobadilla, Melesio Crespo Sánchez

Research output: Contribution to conferencePaper

Abstract

Probabilistic topic models are based upon the idea that documents are mixtures of topics, where a topic is a probability distribution over words. One important feature of those models is the assumption that the only observable variable is the number of times words are produced and their co-occurrence with other words. Representing the content of words and documents with probabilistic topics has one distinct advantage over a purely spatial representation like older approaches like LSA in probabilistic topic modeling. Each topic is individually interpretable, providing a probability distribution over words that picks out a coherent semantic cluster. In this research, we propose to process a set of de-identified transcripts of subjects using a probabilistic topic models technique known as Latent Dirichlet Allocation to create individual semantic models of mental states. Eventually, the modeled mental estates will be derived using Markov Chain Models. Until now, we have created a software project called “Psymantics” capable to apply a full processing to obtain topic models from a set of de-identified transcripts of subjects.
Original languageEnglish
Publication statusPublished - 25 Oct 2019
EventAssociation for the Psychoanalysis of Culture & Society 2019 Annual Conference: Displacement: Precarity & Community - Rutgers University Inn and Conference Center, New Brunswick, United States
Duration: 25 Oct 201927 Oct 2019
https://www.apcsweb.net/annual-conference/

Conference

ConferenceAssociation for the Psychoanalysis of Culture & Society 2019 Annual Conference
Abbreviated titleAPCS 2019
CountryUnited States
CityNew Brunswick
Period25/10/1927/10/19
Internet address

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  • Cite this

    Hafner, D. Z., Molina Villegas, A., Aldana Bobadilla, E. J., & Crespo Sánchez, M. (2019). Semantic Analysis Using Probabilistic Topic Models And Markov Chains. Paper presented at Association for the Psychoanalysis of Culture & Society 2019 Annual Conference, New Brunswick, United States.