Mining topic-level opinion influence in microblog
Proceedings of the 21st ACM international conference on Information and …, 2012•dl.acm.org
This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously
incorporates topic factor, user opinions and social influence in a unified probabilistic model
with two stages learning processes. In the first stage, topic factor and user influence are
integrated to generate users' influential relationship based on different topics; in the second
stage, users' historical messages and social interaction records are leveraged by TOIM to
construct their historical opinions and neighbors' opinion influence through a statistical …
incorporates topic factor, user opinions and social influence in a unified probabilistic model
with two stages learning processes. In the first stage, topic factor and user influence are
integrated to generate users' influential relationship based on different topics; in the second
stage, users' historical messages and social interaction records are leveraged by TOIM to
construct their historical opinions and neighbors' opinion influence through a statistical …
This paper proposes a Topic-Level Opinion Influence Model (TOIM) that simultaneously incorporates topic factor, user opinions and social influence in a unified probabilistic model with two stages learning processes. In the first stage, topic factor and user influence are integrated to generate users' influential relationship based on different topics; in the second stage, users' historical messages and social interaction records are leveraged by TOIM to construct their historical opinions and neighbors' opinion influence through a statistical learning process, which can be further utilized to predict users' future opinions on some specific topics. We evaluate our TOIM on a large-scaled dataset from Tencent Weibo, one of the largest microbloggings website in China. The experimental results show that TOIM can better predict users' opinion than other baseline methods.
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