Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6901298 | Procedia Computer Science | 2017 | 8 Pages |
Abstract
Nowadays information control and detection on the social network have become a problem that we should solve as soon as possible. Unfortunately, due to the informal expressions, detecting the massive data on the internet is a big challenge based on the traditional text mining methods such as Topic Model. In our paper, we propose a simple 4-Tuple Structure instead of the raw text event which usually contains many meaningless words. Using the word embedding technique, we propose the Topic-Specific Information Detection Model (TIDM) for detecting the specific information. For training the words and idiomatic phrases, we adopt the supervise learning technique: manually constructing a specific Semantic Dataset for training our model. Our experiments based on the Amazon Reviews demonstrate that the TIDM can effectively detect and recognize the information.
Related Topics
Physical Sciences and Engineering
Computer Science
Computer Science (General)
Authors
Wen Xu, Jing He, Bo Mao, Youtao Li, Peiqun Liu, Zhiwang Zhang, Jie Cao,