| Issue |
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
|
|
|---|---|---|
| Article Number | 01039 | |
| Number of page(s) | 5 | |
| Section | Network Security System, Neural Network and Data Information | |
| DOI | https://doi.org/10.1051/matecconf/201823201039 | |
| Published online | 19 November 2018 | |
Review of the Classification of Massive Chinese Texts Based on Spark
School of Software Technology, Zhejiang University, Zhejiang, China
a Corresponding author: service@52exe.cn
As the Internet develops rapidly, the number of texts is also growing rapidly. Whether it is the content of online emails exchanged by people, or the online novels and other literary contents, or news reports, personal blogs, Weibo or comments, they are constantly increasing the amount of text at all times. However, most of the data is not classified or processed, which causes a lot of spam, junk information, meaningless articles or advertisements. Their production not only consumes a lot of Internet resources, but also affects users' online experience and reduces the users' work and study efficiency. Therefore, it is vital accurately classify a large amount of text, judge its nature according to the classification result, and carry out targeted treatment. The classification of massive texts based on Spark framework is reviewed in this paper.
© The Authors, published by EDP Sciences, 2018
This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.