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Automatic Extraction of Policy Networks Using Snippets and Social Networks


Affiliations
1 MountZion College of Engineering and Technology, Pudukottai- 622507, Tamil Nadu, India
2 Department of Computer Science Engineering, MountZion College of Engineering and Technology, Pudukottai- 622507, Tamil Nadu, India
 

Background/Objectives: To automatically extract policy networks by using Snippets and Social networks.

Methods/Statistical analysis: The analysis of policy networks demands a series of difficult and time-consuming manual steps including interviews and questionnaires. The approach involves the process of estimating the strength of relations between actors in policy networks using features like webpage counts, out links, and lexical information extracted from data harvested from the web snippets. The approach extracts the irrelevant documents that affect the performance and accuracy. It is overcome by including the process of investigating machine learning algorithms for selecting the most informative metrics.

Findings: The proposed approach includes metrics such as recovery degree, in-link, broken link, anchor text type and kl-divergence and filtering web data based on relevance and type of source, investigating the applicability of proposed metrics for social networks. This enhances the extraction of policy network more accurately.

Improvements/Applications: Overall extraction of policy network is automatic and accurate while using the proposed approach of using Snippets and Social networks.


Keywords

Policy Networks, Relatedness Metrics, Spatial Proximity, Social Proximity.
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  • Automatic Extraction of Policy Networks Using Snippets and Social Networks

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Authors

R. Vidhya
MountZion College of Engineering and Technology, Pudukottai- 622507, Tamil Nadu, India
A. Ajitha
Department of Computer Science Engineering, MountZion College of Engineering and Technology, Pudukottai- 622507, Tamil Nadu, India

Abstract


Background/Objectives: To automatically extract policy networks by using Snippets and Social networks.

Methods/Statistical analysis: The analysis of policy networks demands a series of difficult and time-consuming manual steps including interviews and questionnaires. The approach involves the process of estimating the strength of relations between actors in policy networks using features like webpage counts, out links, and lexical information extracted from data harvested from the web snippets. The approach extracts the irrelevant documents that affect the performance and accuracy. It is overcome by including the process of investigating machine learning algorithms for selecting the most informative metrics.

Findings: The proposed approach includes metrics such as recovery degree, in-link, broken link, anchor text type and kl-divergence and filtering web data based on relevance and type of source, investigating the applicability of proposed metrics for social networks. This enhances the extraction of policy network more accurately.

Improvements/Applications: Overall extraction of policy network is automatic and accurate while using the proposed approach of using Snippets and Social networks.


Keywords


Policy Networks, Relatedness Metrics, Spatial Proximity, Social Proximity.

References