| dc.contributor.author | Ludvigsson, Klas | |
| dc.contributor.author | Andersson, Magnus | |
| dc.date.accessioned | 2020-07-06T09:21:26Z | |
| dc.date.available | 2020-07-06T09:21:26Z | |
| dc.date.issued | 2020-07-06 | |
| dc.identifier.uri | http://hdl.handle.net/2077/65503 | |
| dc.description | In today’s society we are constantly fed information about catastrophic or sad events
through media. While it is important to know about these events, it should be
equally important to also see all the good things that are happening in our world.
Therefore, this thesis proposes two algorithms for classifying full-length news articles
to remove the non-positive articles. Traditionally these types of algorithms require
a large amount of labelled data, but this thesis explores possibilities for sentiment
classification with a limited amount of labelled data. The best performing algorithm
presented is this thesis achieves a precision percentage of 98% with only 40 articles
used for training. | sv |
| dc.description.abstract | In today’s society we are constantly fed information about catastrophic or sad events
through media. While it is important to know about these events, it should be
equally important to also see all the good things that are happening in our world.
Therefore, this thesis proposes two algorithms for classifying full-length news articles
to remove the non-positive articles. Traditionally these types of algorithms require
a large amount of labelled data, but this thesis explores possibilities for sentiment
classification with a limited amount of labelled data. The best performing algorithm
presented is this thesis achieves a precision percentage of 98% with only 40 articles
used for training. | sv |
| dc.language.iso | eng | sv |
| dc.subject | Computer | sv |
| dc.subject | science | sv |
| dc.subject | computer science | sv |
| dc.subject | thesis | sv |
| dc.subject | sentiment | sv |
| dc.subject | classification | sv |
| dc.subject | clustering | sv |
| dc.title | Good News AI Investigating feasibility of categorizing positive sentiment in general news | sv |
| dc.title.alternative | Good News AI Investigating feasibility of categorizing positive sentiment in general news | sv |
| dc.type | text | |
| dc.setspec.uppsok | Technology | |
| dc.type.uppsok | H2 | |
| dc.contributor.department | Göteborgs universitet/Institutionen för data- och informationsteknik | swe |
| dc.contributor.department | University of Gothenburg/Department of Computer Science and Engineering | eng |
| dc.type.degree | Student essay | |
