{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/119084"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/119084","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Teaching machines about emotions","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Felbo, Bjarke"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Program in Media Arts and Sciences (Massachusetts Institute of Technology)","school":null,"contributors":[],"advisors":["Rahwan."],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018","date_published":"2018","updated_at":"2026-07-22T22:22:26Z","subjects":["Program in Media Arts and Sciences ()"],"languages":["eng"],"rights":["MIT theses are protected by copyright. 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