Rule based anonymisation against inference attack in social networks

2021 ◽  
Vol 2 (3) ◽  
pp. 212
Author(s):  
Manik Lal Das ◽  
Nidhi Desai
Keyword(s):  
Social Networks ◽  
Rule Based ◽  
Inference Attack

Aspect Based Sentiment Analysis of Unlabeled Reviews using Linguistic Rule Based LDA

2022 ◽  
Vol 24 (3) ◽  
pp. 0-0
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Rule Based ◽  
Aspect Extraction ◽  
Linguistic Rule ◽  
Digital Era ◽  
Average Accuracy ◽  
Linguistic Rules

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

A Rule-Based Recommendation for Personalization in Social Networks

2014 ◽  
Author(s):  
Rui Zhang ◽  
Yueqi Zhou ◽  
Lin Li ◽  
Chengming Zou
Keyword(s):  
Social Networks ◽  
Rule Based

scholarly journals Structure-Based Sybil Detection in Social Networks via Local Rule-Based Propagation

2019 ◽  
Vol 6 (3) ◽  
pp. 523-537 ◽  
Author(s):  
Binghui Wang ◽  
Jinyuan Jia ◽  
Le Zhang ◽  
Neil Zhenqiang Gong
Keyword(s):  
Social Networks ◽  
Local Rule ◽  
Rule Based

scholarly journals Aspect Based Sentiment Analysis of Unlabeled Reviews Using Linguistic Rule Based LDA

2022 ◽  
Vol 24 (3) ◽  
pp. 1-19
Author(s):  
Nikhlesh Pathik ◽  
Pragya Shukla
Keyword(s):  
Social Networks ◽  
Sentiment Analysis ◽  
Domain Knowledge ◽  
Latent Dirichlet Allocation ◽  
Rule Based ◽  
Aspect Extraction ◽  
Linguistic Rule ◽  
Digital Era ◽  
Average Accuracy ◽  
Linguistic Rules

In this digital era, people are very keen to share their feedback about any product, services, or current issues on social networks and other platforms. A fine analysis of these feedbacks can give a clear picture of what people think about a particular topic. This work proposed an almost unsupervised Aspect Based Sentiment Analysis approach for textual reviews. Latent Dirichlet Allocation, along with linguistic rules, is used for aspect extraction. Aspects are ranked based on their probability distribution values and then clustered into predefined categories using frequent terms with domain knowledge. SentiWordNet lexicon uses for sentiment scoring and classification. The experiment with two popular datasets shows the superiority of our strategy as compared to existing methods. It shows the 85% average accuracy when tested on manually labeled data.

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