Teaching machines about emotions
Name
1057897569-MIT.pdf
Description
Full printable version
Size
5.45 MB
Format
Adobe PDF
Checksum (MD5)
25fd9c95c34acb920affc510b61c7cfb
Author(s)
Felbo, Bjarke
Advisor(s)
Rahwan.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Artificial intelligence algorithms are becoming an increasingly important part of human life with many chat bots and digital personal assistants now interacting directly with us through natural language. Such human-computer interaction can be made more useful by enriching the underlying algorithms with a detailed sense of emotion. In my thesis I propose new ways to detect, encode and modify emotional content in text. First, I show how we can leverage the vast amount of texts on social media with emojis to train a classifier that can accurately detect various kinds of emotional content in text. Secondly, I introduce a state-of-the-art domain adaptation method that is explicitly designed to tackle issues occurring in the messy real-world text data that existing NLP methods struggle with. Lastly, I propose a new algorithm that could be used to decompose text inputs into disentangled representations and then manipulate these representations in a controlled manner to obtain a modified version of the input.
Description
Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 69-77).
Subjects
Program in Media Arts and Sciences ()
MIT Department
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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