Improving sentiment analysis of disaster related social media content

Simple item page
dc.contributor.advisorWei, Wei
dc.contributor.committeeMemberSha, Kewei
dc.contributor.committeeMemberPhillips, Charles E
dc.creatorShivarkar, Pratik
dc.creator.orcid0000-0001-6567-7918
dc.date.accessioned2019-03-11T20:09:46Z
dc.date.available2019-03-11T20:09:46Z
dc.date.created2018-12
dc.date.issued2018-12-14
dc.date.submittedDecember 2018
dc.date.updated2019-03-11T20:09:46Z
dc.description.abstractSocial media platforms have become the most accessible public communication and broadcast channels. Recently, the world has witnessed the prevailing usage of social media for communication during disasters. Being able to monitor and predict public opinions on social media during disasters allows us to evaluate crisis communication theories in order to design more efficient and effective communication mechanisms during the crisis. However, this potential is yet to be materialized due to difficulties in sentiment analysis of social media content. We propose to augment the effectiveness of such analysis by incorporating social relations in sentiment classification models. This thesis extends previous work substantially by looking at social relations of different nature, focusing on different communication goals at each stage of disaster management. This study provides a quantitative analysis of social media sentiments during disaster utilizing improved sentiment analysis and feature extraction techniques.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657.1/1414
dc.language.isoen
dc.subject.lcshSocial media
dc.subject.lcshEmergency management--Data processing
dc.subject.lcshData mining
dc.titleImproving sentiment analysis of disaster related social media content
dc.typeThesis
dc.type.materialtext
thesis.degree.grantorUniversity of Houston-Clear Lake
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
SHIVARKAR-MASTERSTHESIS-2018.pdf
Size:
2.86 MB
Format:
Adobe Portable Document Format
Download

License bundle

Now showing 1 - 2 of 2
No Thumbnail Available
Name:
LICENSE.txt
Size:
1.86 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
PROQUEST_LICENSE.txt
Size:
4.46 KB
Format:
Plain Text
Description:
Download

Collections

Theses and Dissertations
College of Science and Engineering Projects and Theses