  To unfold the tremendous amount of multimedia data uploaded daily to social
media platforms, effective topic modeling techniques are needed. Existing work
tends to apply topic models on written text datasets. In this paper, we propose
a topic extractor on video transcripts. Exploiting neural word embeddings
through graph-based clustering, we aim to improve usability and semantic
coherence. Unlike most topic models, this approach works without knowing the
true number of topics, which is important when no such assumption can or should
be made. Experimental results on the real-life multimodal dataset MuSe-CaR
demonstrates that our approach GraphTMT extracts coherent and meaningful topics
and outperforms baseline methods. Furthermore, we successfully demonstrate the
applicability of our approach on the popular Citysearch corpus.
