Abstract
Big data generated from social media and smart mobile devices has been regarded as a key to obtain insights into human behavior and been extensively utilized for launching marketing activities. A successful marketing activity requires attracting high social popularity to their contents, since higher popularity usually indicates stronger influence, more fame and higher revenue. In this paper, we focus on the question of how to improve popularity of videos sharing on websites like YouTube in mobile computing environment. Obviously, composing high quality titles and tags is beneficial for viewers to discover videos of their interests and increase their tendency to watch more videos. However, it is not an easy task for uploaders, which is especially true since the screen is tight for most mobile devices. To this end, this paper proposes a novel hybrid method based on multi-modal content analysis that recommends keywords for video uploaders to compose titles and tags of their videos and then to gain higher popularity. The method generates candidate keywords by integrating techniques of textual semantic analysis of original tags and recognition of video content. On one hand, taking the original keywords of a video as input, the method obtains most relevant words from WordNet and related video titles gathered from the three top video sharing sites (YouTube, Yahoo Video, Bing Video). On the other hand, through recognizing video content with deep learning technology, the method extracts the entity name of video content as candidate keywords. Finally, a TF-SIM algorithm is proposed to rank the candidate keywords and the most relevant keywords are recommended to uploaders for optimizing the titles and tags of their videos. The experimental results show that the proposed method can effectively improve the social popularity of the videos as well as extend the length of video viewing time per playback.
Highlights
In recent years, mobile computing becomes more and more popular, as reported by Perficient, 58% of web visits were from mobile devices
Our research proposes a hybrid video tagging method based on multi-modal content analysis, which combines the textual semantic relevance analysis and deep learning driven video content recognition to generate candidate video keyword set, and ranks the candidate keywords using a novel keyword-sorting algorithm proposed in the paper to recommend the best keywords to the uploaders
Through investigating the three types of experimental data gathered in YouTube platform, which are view count, viewing time, and the percentage of viewing time, it is demonstrated that the videos optimized with Term FrequencySimilarity (TF-SIM) algorithm can attract higher social popularity and longer viewing time than videos in the original, Normalized Google Distance (NGD) and k-Nearest Neighbor (KNN) algorithm optimized scenarios
Summary
Mobile computing becomes more and more popular, as reported by Perficient, 58% of web visits were from mobile devices. The recommended keywords must be relevant to the video content but not to attract social popularity by deceiving the viewers. On the basis of satisfying the first principle, the method should suggest keywords which are expected to attract high social popularity. The proposed method applies deep learning driven image recognition technology to extract the entity names from video content as candidate keywords as well. According to the second principle, the paper proposes a novel algorithm named Term FrequencySimilarity (TF-SIM) to sort the collection of candidate keywords related to the video. (1) The paper proposes a method for suggesting keywords that are relevant to video content. The comparative experimental results show that the titles and tags optimized using our method can effectively boost the social popularity and extend viewing time of a video
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