Digital Library[ Search Result ]
Proposal of a Graph Based Chat Message Analysis Model for Messenger User Verification
http://doi.org/10.5626/JOK.2022.49.9.696
As crimes and accidents through messengers increase, the necessity of verifying messenger users is emerging. In this study, two graph-based messenger user verification models that apply the traditional author verification problem to chat text were proposed. First, the graph random walk model builds an n-gram transition graph with a previous chat message and verifies the user by learning the characteristic of traversing the transition graph with a message whose author is unknown. The results showed an accuracy of 86% in 10,000 chat conversations. Second, the graph volume model verified the user using the characteristic that the size of the transition graph increased over time and achieved an accuracy of 87% in 1,000 chat conversations. When the density of the chat messages was calculated based on the transmission time, both graph models could guarantee more than 80% accuracy when the chat density was 15 or more.
A Cross-Texting Prevention System Using Syntactic Characteristics of Chat Messages
http://doi.org/10.5626/JOK.2021.48.6.639
Cross-texting refers to accidentally sending a message to an unintended person. It occurs frequently when users chat with multiple counterparts at the same time. Messengers mainly provide a function of canceling sending, but it is only post solution, and users find it difficult to prevent mistakes in advance. In this paper, we proposed a cross-texting detection model by analyzing the syntactic characteristics of chat sentences. It modelizes the previous chat messages of a specific user by extracting the honorifics and completeness features from chat messages, and detects the cross-texting cases by determining whether the target sentences are in accordance with the user chat message model. This approach is significant as it solves the cross-texting detection problem only with syntactic characteristics without semantic analysis by modeling the consistency of the user"s chat attitude. The proposed model detect cross-texting cases with an accuracy of 85.5% from automatically generated data using a real messenger dialogue corpus.
Keyword Network Visualization for Text Summarization and Comparative Analysis
Kyeong-rim Kim, Da-yeong Lee, Hwan-Gue Cho
Most of the information prevailing in the Internet space consists of textual information. So one of the main topics regarding the huge document analyses that are required in the “big data” era is the development of an automated understanding system for textual data; accordingly, the automation of the keyword extraction for text summarization and abstraction is a typical research problem. But the simple listing of a few keywords is insufficient to reveal the complex semantic structures of the general texts. In this paper, a text-visualization method that constructs a graph by computing the related degrees from the selected keywords of the target text is developed; therefore, two construction models that provide the edge relation are proposed for the computing of the relation degree among keywords, as follows: influence-interval model and word- distance model. The finally visualized graph from the keyword-derived edge relation is more flexible and useful for the display of the meaning structure of the target text; furthermore, this abstract graph enables a fast and easy understanding of the target text. The authors’ experiment showed that the proposed abstract-graph model is superior to the keyword list for the attainment of a semantic and comparitive understanding of text.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr