Intelligent Classifier for Identify Reliable On-Demand Messages

Jiann-Liang Chen,
Yi-Wei Ma,
Song-Yun Tsai,


Accurately extracting useful messages from bodies of information is important. This work proposes an intelligent system, called AI@nti-Fake system, to categories social news and determine whether it is true or false. The news is preprocessed using a Natural Language Processing technique. The text sentiment analysis in the on-demand message is analyzed to identify the fake news. A dataset from the International Workshop on Semantic Evaluation is used in this study. The on-demand message is related to the public’s attention, and the analyzed text sentiment is identified as positive, neutral or negative. The accuracies of the proposed AI@nti-Fake system in the training stage and the real data test can reach 90% and 80%, respectively. The F1-Score of the proposed approach and two others methods are 78.50, 64.84 and 64.59, respectively. The results of the analysis reveal that the F1-Score of our approach can get better performance in classifying on-demand messages and detecting disinformation. The proposed AI@nti-Fake system, which is based on social media analysis and the judgment of sentiment may have applications in business.

Citation Format:
Jiann-Liang Chen, Yi-Wei Ma, Song-Yun Tsai, "Intelligent Classifier for Identify Reliable On-Demand Messages," Journal of Internet Technology, vol. 21, no. 7 , pp. 1993-1997, Dec. 2020.

Full Text:



  • There are currently no refbacks.

Published by Executive Committee, Taiwan Academic Network, Ministry of Education, Taipei, Taiwan, R.O.C
JIT Editorial Office, Office of Library and Information Services, National Dong Hwa University
No. 1, Sec. 2, Da Hsueh Rd., Shoufeng, Hualien 974301, Taiwan, R.O.C.
Tel: +886-3-931-7314  E-mail: