Extracting Dimension Hierarchy of Tweeters Interests for On-line Analytical Processing

YU Dongjin; NI Zhiyong; SUN Jingchao

doi:10.11999/JEIT170030

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2081-2088

YU Dongjin, NI Zhiyong, SUN Jingchao. Extracting Dimension Hierarchy of Tweeters Interests for On-line Analytical Processing[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2081-2088. doi: 10.11999/JEIT170030

Citation:

YU Dongjin, NI Zhiyong, SUN Jingchao. Extracting Dimension Hierarchy of Tweeters Interests for On-line Analytical Processing[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2081-2088. doi: 10.11999/JEIT170030

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Extracting Dimension Hierarchy of Tweeters Interests for On-line Analytical Processing

doi: 10.11999/JEIT170030

Funds:

The National Natural Science Foundation of China (61100043, 61472112), The Natural Science Foundation of Zhejiang Province (LY12F02003), The Key Science and Technology Project of Zhejiang Province (2017C01010, 2016F50014)

Received Date: 2017-01-11
Rev Recd Date: 2017-08-16
Publish Date: 2017-09-19

Abstract

Abstract

To explore the distribution and correlation from massive Twitter data helps the accurate personalized recommendation. On-Line Analytical Processing (OLAP) provides an intuitive form that is suitable for people to explore the Twitter data. The key of applying OLAP to Twitter data is how to mine and build dimension hierarchy of tweeter interests. Different from the existing approaches that can extract interests of tweeters with only one level, an approach to the extraction of dimension hierarchy of interests for OLAP is proposed. Firstly, it retrieves the Twitter data through RestAPI. Afterwards, it detects the interests and sub-interests using an improved (Latent Dirichlet Allocation, LDA) model. Based on the extracted interests and sub-interests it finally constructs the dimension hierarchy of interests. The experiment verifies its effectiveness and scalability, and demonstrates it can extract dimension hierarchy of tweeters interests for OLAP more effectively than LDA and hLDA.
- On-Line Analytical Processing (OLAP),
- Twitter,
- Dimension hierarchy,
- Interests,
- Latent Dirichlet Allocation (LDA) model

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References(17)

References

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