Computer Classification of News
| dc.contributor.author | Liu, Siyi | |
| dc.date.accessioned | 2024-03-16T14:24:22Z | |
| dc.date.available | 2024-03-16T14:24:22Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | Facing the growing demand for psychological services, if we want computers to provide psychological services to humans, the first step is to identify human emotions accurately. This research is about how to enable computers to accurately identify text content, analyze it, and make judgments. In this process, the torchtext.datasets.AG_NEWS dataset was selected. AG_NEWS has four large classes ("World", "Sports", "Business", "Sci/Tech"). TfidfVectorizer was used to judge the importance of each word in a sentence. Common words like articles frequently appear in every sentence, so they are ignored. The importance of other words for sentence classification is judged based on their frequency of appearance – the higher the frequency of a word, the less weight it carries. Support Vector Machines are then used to optimize accuracy. The research showed AI tools can predict human emotions based on the interpretation of the text. If AI tools can accurately predict human emotions, this builds a foundation for the machines to respond appropriately to people needing psychological services. | |
| dc.description.reviewstatus | Reviewed | |
| dc.description.scholarlevel | Undergraduate | |
| dc.description.sponsorship | Jamie Cassels Undergraduate Research Awards (JCURA) | |
| dc.identifier.uri | https://hdl.handle.net/1828/16132 | |
| dc.language.iso | en | |
| dc.publisher | University of Victoria | |
| dc.subject | machine learning | |
| dc.subject | data mining | |
| dc.subject | deep learning | |
| dc.subject | artificial intelligence | |
| dc.subject | text classification | |
| dc.title | Computer Classification of News | |
| dc.type | Poster |