{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101090"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101090","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Affective analysis of text in tweets","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Narwekar, Abhishek Avinash"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Girju, Roxana"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:32:02Z","date_published":"2018-09-04T20:32:02Z","updated_at":"2026-07-22T22:24:38Z","subjects":["affective computing, emotion detection, sentiment analysis, text processing, emotion in tweets"],"languages":["en"],"rights":["Copyright 2018 Abhishek Avinash Narwekar"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101090","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Girju, Roxana"]},{"key":"dc:creator","label":"Author","values":["Narwekar, Abhishek Avinash"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:32:02Z","2018-04-26","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["affective computing, emotion detection, sentiment analysis, text processing, emotion in tweets"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Abhishek Avinash Narwekar"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101090"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Affective analysis of text in tweets"]}]}],"canonical_facts":{"dc:contributor":["Girju, Roxana"],"dc:creator":["Narwekar, Abhishek Avinash"],"dc:date":["2018-09-04T20:32:02Z","2018-04-26","2018-05"],"dc:description":["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). 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