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An Improved Method for Web Text Affective Cognition Computing Based on Knowledge Graph

Bohan Niu1,*, Yongfeng Huang2

Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.
Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.

* Corresponding Author: Bohan Niu. Email: email.

Computers, Materials & Continua 2019, 59(1), 1-14. https://doi.org/10.32604/cmc.2019.06032

Abstract

The goal of research on the topics such as sentiment analysis and cognition is to analyze the opinions, emotions, evaluations and attitudes that people hold about the entities and their attributes from the text. The word level affective cognition becomes an important topic in sentiment analysis. Extracting the (attribute, opinion word) binary relationship by word segmentation and dependency parsing, and labeling those by existing emotional dictionary combined with webpage information and manual annotation, this paper constitutes a binary relationship knowledge base. By using knowledge embedding method, embedding each element in (attribute, opinion, opinion word) as a word vector into the Knowledge Graph by TransG, and defining an algorithm to distinguish the opinion between the attribute word vector and the opinion word vector. Compared with traditional method, this engine has the advantages of high processing speed and low occupancy, which makes up the time-costing and high calculating complexity in the former methods.

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Cite This Article

APA Style
Niu, B., Huang, Y. (2019). An improved method for web text affective cognition computing based on knowledge graph. Computers, Materials & Continua, 59(1), 1-14. https://doi.org/10.32604/cmc.2019.06032
Vancouver Style
Niu B, Huang Y. An improved method for web text affective cognition computing based on knowledge graph. Comput Mater Contin. 2019;59(1):1-14 https://doi.org/10.32604/cmc.2019.06032
IEEE Style
B. Niu and Y. Huang, "An Improved Method for Web Text Affective Cognition Computing Based on Knowledge Graph," Comput. Mater. Contin., vol. 59, no. 1, pp. 1-14. 2019. https://doi.org/10.32604/cmc.2019.06032

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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