2026-09-20T00:11:57Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1131312026-06-06T00:55:18Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Andrew W. Lo.
Liu, Clare H
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-01-12T20:58:20Z
2018-01-12T20:58:20Z
2017
2017
http://hdl.handle.net/1721.1/113131
1017570331
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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 71-76).
Currently, most applications of sentiment analysis focus on detecting sentiment polarity, which is whether a piece of text can be classified as positive or negative. However, it can sometimes be important to be able to distinguish between distinct emotions as opposed to just the polarity. In this thesis, we use a supervised learning approach to develop an emotion classifier for the six Ekman emotions: joy, fear, sadness, disgust, surprise, and anger. Then we apply our emotion classifier to tweets from the 2016 presidential election and financial tweets labeled with Twitter cashtags and evaluate the effectiveness of using finer-grained emotion categorization to predict future stock market performance.
by Clare H. Liu.
M.Eng.
76 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Applications of twitter emotion detection for stock market prediction
Thesis
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