When conducting research, it is valuable to find high-ranked papers closely related to the specific research area, without spending too much time reading insignificant papers. To make this process more effective an automated process to extract topics from documents would be useful, and this is possible using topic modeling. Topic modeling can also be used to provide topic trends, where a topic is first mentioned, and who the original author was. In this paper, over 5000 articles are scraped from four different top-ranked internet security conferences, using a web scraper built in Python. From the articles, fourteen topics are extracted, using the topic modeling library Gensim and LDA Mallet, and the topics are visualized in graphs to find trends about which topics are emerging and fading away over twenty years. The result found in this research is that topic modeling is a powerful tool to extract topics, and when put into a time perspective, it is possible to identify topic trends, which can be explained when put into a bigger context.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-177748 |
| Date | January 2021 |
| Creators | Johansson, Richard, Engström Heino, Otto |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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