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). / 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. / by Bjarke Felbo. / S.M.
| Identifer | oai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/119084 |
| Date | January 2018 |
| Creators | Felbo, Bjarke |
| Contributors | Rahwan., Program in Media Arts and Sciences (Massachusetts Institute of Technology), Program in Media Arts and Sciences (Massachusetts Institute of Technology) |
| Publisher | Massachusetts Institute of Technology |
| Source Sets | M.I.T. Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 77 pages, application/pdf |
| Rights | MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission., http://dspace.mit.edu/handle/1721.1/7582 |
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