On the Place of Text Data in Lifelogs, and Text Analysis via Semantic Facets

TL;DR

This paper explores the integration of textual data in lifelogging devices to analyze cognitive activities through semantic facets, proposing methods for automatic taxonomy creation to quantify intellectual engagement.

Contribution

It introduces a novel approach to analyze textual lifelog data using semantic facets and develops methods for automatic taxonomy generation for cognitive activity detection.

Findings

Semantic analysis can quantify cognitive activity in lifelog data.

Taxonomic subject facets help in understanding intellectual focus.

Proposed methods facilitate automatic creation of topic vocabularies.

Abstract

Current research in lifelog data has not paid enough attention to analysis of cognitive activities in comparison to physical activities. We argue that as we look into the future, wearable devices are going to be cheaper and more prevalent and textual data will play a more significant role. Data captured by lifelogging devices will increasingly include speech and text, potentially useful in analysis of intellectual activities. Analyzing what a person hears, reads, and sees, we should be able to measure the extent of cognitive activity devoted to a certain topic or subject by a learner. Test-based lifelog records can benefit from semantic analysis tools developed for natural language processing. We show how semantic analysis of such text data can be achieved through the use of taxonomic subject facets and how these facets might be useful in quantifying cognitive activity devoted to various topics in a person's day. We are currently developing a method to automatically create taxonomic topic vocabularies that can be applied to this detection of intellectual activity.