  Time-sync comments reveal a new way of extracting the online video tags.
However, such time-sync comments have lots of noises due to users' diverse
comments, introducing great challenges for accurate and fast video tag
extractions. In this paper, we propose an unsupervised video tag extraction
algorithm named Semantic Weight-Inverse Document Frequency (SW-IDF).
Specifically, we first generate corresponding semantic association graph (SAG)
using semantic similarities and timestamps of the time-sync comments. Second,
we propose two graph cluster algorithms, i.e., dialogue-based algorithm and
topic center-based algorithm, to deal with the videos with different density of
comments. Third, we design a graph iteration algorithm to assign the weight to
each comment based on the degrees of the clustered subgraphs, which can
differentiate the meaningful comments from the noises. Finally, we gain the
weight of each word by combining Semantic Weight (SW) and Inverse Document
Frequency (IDF). In this way, the video tags are extracted automatically in an
unsupervised way. Extensive experiments have shown that SW-IDF (dialogue-based
algorithm) achieves 0.4210 F1-score and 0.4932 MAP (Mean Average Precision) in
high-density comments, 0.4267 F1-score and 0.3623 MAP in low-density comments;
while SW-IDF (topic center-based algorithm) achieves 0.4444 F1-score and 0.5122
MAP in high-density comments, 0.4207 F1-score and 0.3522 MAP in low-density
comments. It has a better performance than the state-of-the-art unsupervised
algorithms in both F1-score and MAP.
