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Social media Key Opinion Leader (KOL) is very popular with advertisers because of their excellent business value. However, with the low entry threshold of the KOL industry and data fraud, advertisers are unable to find a KOL that matches their own brand quickly. Based on the above background, this paper studies the video released by KOL on social platforms, analyzes the dynamic theme of the barrage text in the video, and describes the change of the theme of the barrage over time. At the same time, a convolutional neural network model is used to perform sentiment analysis on the barrage text of the video containing the advertisement, and further analyze the audience's emotional polarity to the situation that the video released by KOL containing the advertisement. The experimental results show that the proposed KOL analysis method evaluates the commercial value of KOL more comprehensively and specifically, helping advertisers find a suitable KOL efficiently. [ABSTRACT FROM AUTHOR]

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In some complex scenes of tracking, the traditional color feature based target tracking algorithm may have the unstable tracking. To solve this problem, a color and texture feature based target tracking algorithm is proposed. The texture feature is added in the traditional color Mean-shift based target tracking algorithm, and both the texture and the color features are extracted together. According to the sequence principle, the traditional color based mean drift tracking algorithm is still used to search the new position of the target, but, in each iteration process, the texture feature is used to search the optimal target position which has the largest similar feature. And the eight neighborhood search method(8areas with the same size are expanded around the candidate area)is applied to solve the problem of partial occlusion. The comparative experiments show that the algorithm has better robustness and realtime performance in complex scenes. [ABSTRACT FROM AUTHOR]