2,581 research outputs found
Words are Malleable: Computing Semantic Shifts in Political and Media Discourse
Recently, researchers started to pay attention to the detection of temporal
shifts in the meaning of words. However, most (if not all) of these approaches
restricted their efforts to uncovering change over time, thus neglecting other
valuable dimensions such as social or political variability. We propose an
approach for detecting semantic shifts between different viewpoints--broadly
defined as a set of texts that share a specific metadata feature, which can be
a time-period, but also a social entity such as a political party. For each
viewpoint, we learn a semantic space in which each word is represented as a low
dimensional neural embedded vector. The challenge is to compare the meaning of
a word in one space to its meaning in another space and measure the size of the
semantic shifts. We compare the effectiveness of a measure based on optimal
transformations between the two spaces with a measure based on the similarity
of the neighbors of the word in the respective spaces. Our experiments
demonstrate that the combination of these two performs best. We show that the
semantic shifts not only occur over time, but also along different viewpoints
in a short period of time. For evaluation, we demonstrate how this approach
captures meaningful semantic shifts and can help improve other tasks such as
the contrastive viewpoint summarization and ideology detection (measured as
classification accuracy) in political texts. We also show that the two laws of
semantic change which were empirically shown to hold for temporal shifts also
hold for shifts across viewpoints. These laws state that frequent words are
less likely to shift meaning while words with many senses are more likely to do
so.Comment: In Proceedings of the 26th ACM International on Conference on
Information and Knowledge Management (CIKM2017
Joint Topic-Semantic-aware Social Recommendation for Online Voting
Online voting is an emerging feature in social networks, in which users can
express their attitudes toward various issues and show their unique interest.
Online voting imposes new challenges on recommendation, because the propagation
of votings heavily depends on the structure of social networks as well as the
content of votings. In this paper, we investigate how to utilize these two
factors in a comprehensive manner when doing voting recommendation. First, due
to the fact that existing text mining methods such as topic model and semantic
model cannot well process the content of votings that is typically short and
ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to
learn word and document representation by jointly considering their topics and
semantics. Then we propose our Joint Topic-Semantic-aware social Matrix
Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates
similarity among users and votings by combining their TEWE representation and
structural information of social networks, and preserves this
topic-semantic-social similarity during matrix factorization. To evaluate the
performance of TEWE representation and JTS-MF model, we conduct extensive
experiments on real online voting dataset. The results prove the efficacy of
our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge
Management (CIKM 2017
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