2 research outputs found
Time-sync Video Tag Extraction Using Semantic Association Graph
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.Comment: Accepted by ACM TKDD 201
Interactive Variance Attention based Online Spoiler Detection for Time-Sync Comments
Nowadays, time-sync comment (TSC), a new form of interactive comments, has
become increasingly popular in Chinese video websites. By posting TSCs, people
can easily express their feelings and exchange their opinions with others when
watching online videos. However, some spoilers appear among the TSCs. These
spoilers reveal crucial plots in videos that ruin people's surprise when they
first watch the video. In this paper, we proposed a novel Similarity-Based
Network with Interactive Variance Attention (SBN-IVA) to classify comments as
spoilers or not. In this framework, we firstly extract textual features of TSCs
through the word-level attentive encoder. We design Similarity-Based Network
(SBN) to acquire neighbor and keyframe similarity according to semantic
similarity and timestamps of TSCs. Then, we implement Interactive Variance
Attention (IVA) to eliminate the impact of noise comments. Finally, we obtain
the likelihood of spoiler based on the difference between the neighbor and
keyframe similarity. Experiments show SBN-IVA is on average 11.2\% higher than
the state-of-the-art method on F1-score in baselines.Comment: Accepted by CIKM 201