Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019)

Prediction Model based on Internet News Buzzword Data

Authors
Xuan Lei
Corresponding Author
Xuan Lei
Available Online April 2019.
DOI
https://doi.org/10.2991/icmeit-19.2019.16How to use a DOI?
Keywords
network news; feature selection; classification algorithm; model evaluation.
Abstract
The partial classification algorithm is mainly used to predict the popularity of network news and to explore the best model to predict the popularity of network news, so as to help network news service providers predict the popularity of news before publication. The popularity of network news is predicted according to the data analysis process: first, UCI data sets are pre-processed; secondly, feature selection is conducted for the data sets by using recursive feature elimination algorithm; then modelling and analysis is carried out, and finally through the confusion matrix, risk map and ROC (Receiver Operating Characteristic) chart performance evaluation, the performance of the model is compared and analyzed. Through comparison, it is found that random forest is the best prediction model.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
Part of series
Advances in Computer Science Research
Publication Date
April 2019
ISBN
978-94-6252-708-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icmeit-19.2019.16How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Xuan Lei
PY  - 2019/04
DA  - 2019/04
TI  - Prediction Model based on Internet News Buzzword Data
PB  - Atlantis Press
SP  - 89
EP  - 95
SN  - 2352-538X
UR  - https://doi.org/10.2991/icmeit-19.2019.16
DO  - https://doi.org/10.2991/icmeit-19.2019.16
ID  - Lei2019/04
ER  -