2026-09-19T08:08:02Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/101090 2023-07-11 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

Girju, Roxana Narwekar, Abhishek Avinash 2018-09-04T20:32:02Z 2018-09-04T20:32:02Z 2018-04-26 2018-05 Affective computing is the study and development of devices that can recognize emotions through various modes such as video, audio and text automatically. In this thesis, I focus on the problem of affective computing in short texts, in particular, tweets. With the evolution of social media in the recent years, there has been a rapid growth of interactions that take occur online, which are expressive in terms of emotion. Internet users today have several diverse methods of being expressive through text, such as by using abbreviations, emoticons and hashtags. I use traditional lexical features and word embeddings to extract semantic and lexical information from the input text. I develop models ranging from linear and tree-based models to deep neural networks to perform emotion detection on Tweets. I create an ensemble of these methods to make my final predictions. I evaluate the ensemble on the SemEval 2018 dataset containing intensity and class annotations for emotions in tweets. I finally perform an error analysis of these algorithms and highlight potential areas of improvement. Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms The student, Abhishek Avinash Narwekar, accepted the attached license on 2018-04-26 at 10:46. The student, Abhishek Avinash Narwekar, submitted this Thesis for approval on 2018-04-26 at 10:52. This Thesis was approved for publication on 2018-04-26 at 17:28. DSpace SAF Submission Ingestion Package generated from Vireo submission #12519 on 2018-08-31 at 17:15:09 Made available in DSpace on 2018-09-04T20:32:02Z (GMT). No. of bitstreams: 2 NARWEKAR-THESIS-2018.pdf: 1490120 bytes, checksum: cd15e7108b1752796f663a770a9117fb (MD5) LICENSE.txt: 4222 bytes, checksum: 29c2dbd158ee7e760226f6b66e27c78c (MD5) Previous issue date: 2018-04-26 application/pdf http://hdl.handle.net/2142/101090 en Copyright 2018 Abhishek Avinash Narwekar affective computing, emotion detection, sentiment analysis, text processing, emotion in tweets Affective analysis of text in tweets text text Computer Science Computer Science University of Illinois at Urbana-Champaign Thesis M.S.