| dc.contributor.advisor | Ahmad, Khurshid | |
| dc.contributor.author | Daly, Nicholas | |
| dc.date.accessioned | 2017-02-23T16:45:48Z | |
| dc.date.available | 2017-02-23T16:45:48Z | |
| dc.date.issued | 2014 | |
| dc.identifier.citation | Nicholas Daly, 'Generating sentiment lexica : evaluating approaches with genetic algorithms and particle swarm optimization', [thesis], Trinity College (Dublin, Ireland). School of Computer Science & Statistics, 2014, pp 174 | |
| dc.identifier.other | THESIS 10748 | |
| dc.identifier.uri | http://hdl.handle.net/2262/79552 | |
| dc.description.abstract | This thesis examines the application of sentiment analysis towards financial news. The primary goal within the field of sentiment analysis is to develop a methodology by which the sentiment or emotional view being expressed by the author(s) of a text may be extracted and quantified. | |
| dc.format | 1 volume | |
| dc.language.iso | en | |
| dc.publisher | Trinity College (Dublin, Ireland). School of Computer Science & Statistics | |
| dc.relation.isversionof | http://stella.catalogue.tcd.ie/iii/encore/record/C__Rb16196091 | |
| dc.subject | Computer Science, Ph.D. | |
| dc.subject | Ph.D. Trinity College Dublin | |
| dc.title | Generating sentiment lexica : evaluating approaches with genetic algorithms and particle swarm optimization | |
| dc.type | thesis | |
| dc.type.supercollection | thesis_dissertations | |
| dc.type.supercollection | refereed_publications | |
| dc.type.qualificationlevel | Doctoral | |
| dc.type.qualificationname | Doctor of Philosophy (Ph.D.) | |
| dc.rights.ecaccessrights | openAccess | |
| dc.format.extentpagination | pp 174 | |
| dc.description.note | TARA (Trinity’s Access to Research Archive) has a robust takedown policy. Please contact us if you have any concerns: rssadmin@tcd.ie | |
