Recognizing Users Focuses on Social Network Based on Mixed-weight Combined Strategy

JI Jianrui; LIU Yezheng; JIANG Yuanchun

doi:10.11999/JEIT161348

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2056-2062

JI Jianrui, LIU Yezheng, JIANG Yuanchun. Recognizing Users Focuses on Social Network Based on Mixed-weight Combined Strategy[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2056-2062. doi: 10.11999/JEIT161348

Citation:

JI Jianrui, LIU Yezheng, JIANG Yuanchun. Recognizing Users Focuses on Social Network Based on Mixed-weight Combined Strategy[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2056-2062. doi: 10.11999/JEIT161348

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Recognizing Users Focuses on Social Network Based on Mixed-weight Combined Strategy

doi: 10.11999/JEIT161348 cstr: 32379.14.JEIT161348

Funds:

The National Natural Science Foundation of China (71490725, 71521001, 71371062, 91546114, 71501057), The National 973 Program of China (2013CB329603), The National Key Technology Support Program (2015BAH26F00), MOE Project of Humanities and Social Sciences (15YJC630111)

Received Date: 2016-12-09
Rev Recd Date: 2017-05-12
Publish Date: 2017-09-19

Abstract

Abstract

It is an important measure to utilize the topic model to recognize the users focuses on social networks, such as blog, online community, and microblog. Considering the particularity of topic recognizing of short texts on the social network platform, this paper develops an AW-LDA model based on mixed-weight combined strategy according to the relevance of short texts context. This model virtually combines short texts, which are in line with contextual-related conditions, and endows different short texts with different weights according to the related extent. It proposes a new method of recognizing short texts topics. According to the experiments on data of BBS and Weibo communities, the results show that the model can effectively recognize social network users focuses on different subjects and it proposes a new idea about solving the topic recognition problem of short texts.
- Social network,
- Topic model,
- Focus recognition,
- Mix-weight,
- Neighboringuser

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References(14)

References

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