2026-09-19T08:51:34Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1060322026-06-06T00:54:58Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Patrick Henry Winston.
Alexander, Ryan Cherian
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T15:48:36Z
2016-12-22T15:48:36Z
2016
2016
http://hdl.handle.net/1721.1/106032
965196011
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 61-62).
Emotions greatly influence human cognition. Therefore, if we are to develop artificially intelligent programs that work closely with humans, we must ensure that they are capable of empathy. In an effort to realize the goal of emotionally aware programs, I created a multi-corpus informed vector space model to determine the emotions evoked by individual terms. I then combined that information with the semantic parse trees produced by the Genesis Story Understanding System to ascertain the emotions evoked by a single sentence. Additionally, I used the story aligner within Genesis to determine the emotions evoked by stories described over multiple sentences. My program can infer characters' emotional states based on their descriptions, the situations they are involved in, and the actions they perform. For instance, it infers that Alice is joyful from the sentence "Alice wins an award" and that James is probably experiencing sadness from the sentence "James is lonely." Additionally, the program can identify that Austin is likely surprised if "Austin has to take a test" and "Austin doesn't know about the test."
by Ryan Cherian Alexander.
M.Eng.
62 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Artificial empathy : using vector space modeling and mixed scope alignment to infer emotional states of characters in stories
Thesis
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<Abstract>Emotions greatly influence human cognition. Therefore, if we are to develop artificially intelligent programs that work closely with humans, we must ensure that they are capable of empathy. In an effort to realize the goal of emotionally aware programs, I created a multi-corpus informed vector space model to determine the emotions evoked by individual terms. I then combined that information with the semantic parse trees produced by the Genesis Story Understanding System to ascertain the emotions evoked by a single sentence. Additionally, I used the story aligner within Genesis to determine the emotions evoked by stories described over multiple sentences. My program can infer characters' emotional states based on their descriptions, the situations they are involved in, and the actions they perform. For instance, it infers that Alice is joyful from the sentence "Alice wins an award" and that James is probably experiencing sadness from the sentence "James is lonely." Additionally, the program can identify that Austin is likely surprised if "Austin has to take a test" and "Austin doesn't know about the test."</Abstract>
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