Movie summarization using bullet screen comments

Sun, Shan; Wang, Feng; He, Liang

doi:10.1007/s11042-017-4807-6

Cited by 17 publications

(13 citation statements)

References 34 publications

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“…And we can establish a scoring mechanism to score for the platform, to facilitate the timely feedback and improvement of live streaming platform. It provides the basis of different angles for short-video cutting and recommendation [36]. Whether the current video content is wonderful or brilliant is measured by the number and content of danmaku in a period time.…”

Section: Comparative Experiments With Sotamentioning

confidence: 99%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

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With the rapid development of social media, danmaku video provides a platform for users to communicate online. To some extent, danmaku video provides emotional timing information and an innovative method to analyze video data. In the age of big data, studying the characteristics of danmaku and its emotional tendencies can not only help us understand the psychological characteristics of users but also feedback the effective information of users to video platforms, which can help the platforms optimize related short video recommendations so that it can provide a more accurate solution for the selection of audiences during video production. However, danmaku is different from traditional comments. Current emotion classification methods are only suitable for two-dimensional classification which are not suitable for danmaku emotion analysis. Aiming at the problems such as the colloquialism, diversity, spelling errors, structural non-linearity informal language on the Internet, diversity of social topics, and context dependency of emotion analysis of the danmaku data, this paper proposes an improved emotion analysis model based on Bi-LSTM model to classify the further four-dimensional emotions of Pleasure, Anger, Sorrow and Joy. Furthermore, we add tags such as comment time and user name to the danmaku information. Experimental results show that the improved model has higher Accuracy, Recall, Precision, and F1-Score under the same conditions compared with the CNN and SVM. The classification effect of improved model is close to the SOTA. Experimental results also show that the improved model can be effectively applied to the analysis of irregular danmaku emotion.

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Section: Comparative Experiments With Sotamentioning

confidence: 99%

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

et al. 2020

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“…To utilize statistical information of danmaku, text information and timing characteristics are also included for the extraction of video highlights [10], [14]- [16]. Firstly, the concentration of the topics contained in the danmaku can be used to dynamically segment the video; secondly, the number of danmaku that changes over time also reflects the trend of the highlights on different parts of the video.…”

Section: ) Extraction Of Video Highlight Segmentsmentioning

confidence: 99%

Stories That Big Danmaku Data Can Tell as a New Media

Bai

et al. 2019

IEEE Access

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In online video communities, an emerging source of comment, such as danmaku, allows viewers to interact when watching. Prior work discussed the feasibility of application using danmaku, while the comprehensive analysis of large-scale data is vacant to be filled in. We here release our danmaku data collection and report interesting observed phenomena in the danmaku comments. This analysis reveals the temporal distributions and user's access patterns of online time-sync comments. In particular, we distribute two novel natural language processing (NLP) tasks based on our danmaku dataset and provide baseline models. In the first task, we show how the naive models predict positive or negative sentiment given a danmaku comment, which effectively extends the real applications such as opinion poll prediction and marketing investigation. In the second task, we propose to use the NLP summarization model to make video tagging and summarization. The experimental results suggest that danmaku can not only support deeper and richer interactions between viewers and videos but also with high research value.INDEX TERMS Danmaku, HCI, big danmaku data, text tagging, sentiment analysis, summarization.

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“…Obviously, these expressions and tones all express the rich feelings of the barrage sender. However, most of existing methods do not consider the influence of the expression and tone on barrage sentiment analysis [4], [6], [7], [9]- [11].…”

Section: B the Concept Of Barragementioning

confidence: 99%

“…The associate editor coordinating the review of this manuscript and approving it for publication was Dalin Zhang. of existing researches on the barrage regard it as a time synchronization text tag of a closely related video [5]. The barrages are mainly used in video content abstract [6], video content tag [7], video key frame recommendation [8], etc. Because of the characteristics of the barrage itself, such as short text content, colloquial, more network language, nonstandard language and so on, the sentiment analysis of the barrage still faces great challenges.…”

Section: Introductionmentioning

confidence: 99%

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A Barrage Sentiment Analysis Scheme Based on Expression and Tone

et al. 2019

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Most of existing methods do not consider the influence of expression and tone on barrage sentiment analysis. This decreases the effect and accuracy of barrage sentiment analysis. Therefore, we propose a barrage sentiment analysis scheme based on expression and tone. First, we propose a new sentiment dictionary based on expression and tone to increase the effect of barrage sentiment analysis. Second, we propose a new calculation method of sentiment value based on expression and tone for barrage sentiment analysis to increase the accuracy. Meanwhile, we change a single threshold to a threshold range, to expand the scope of neutral barrages. Finally, the experimental results show that our scheme is more effective and practical than the existing methods in the scenario of barrage sentiment analysis.

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Movie summarization using bullet screen comments

Cited by 17 publications

References 34 publications

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Improved Danmaku Emotion Analysis and Its Application Based on Bi-LSTM Model

Stories That Big Danmaku Data Can Tell as a New Media

A Barrage Sentiment Analysis Scheme Based on Expression and Tone

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