Design and Implementation of Weibo Sentiment Analysis Based on LDA and Dependency Parsing

Information content security is a branch of cyberspace security. How to effectively manage and use Weibo comment information has become a research focus in the field of information content security. Three main tasks involved are emotion sentence identification and classification,emotion tendency classification,and emotion expression extraction. Combining with the latent Dirichlet allocation(LDA) model,a Gibbs sampling implementation for inference of our algorithm is presented,and can be used to categorize emotion tendency automatically with the computer. In accordance with the lower ratio of recall for emotion expression extraction in Weibo,use dependency parsing,divided into two categories with subject and object,summarized six kinds of dependency models from evaluating objects and emotion words,and proposed that a merge algorithm for evaluating objects can be accurately evaluated by participating in a public bakeoff and in the shared tasks among the best methods in the sub-task of emotion expression extraction,indicating the value of our method as not only innovative but practical.

Hiding Information in MPEG Sequences by Using of B-Frames

In this paper,in order to increase the amount of hiding information,we choose MPEG sequences as carriers and present a practical method focus on the steganographic method in MPEG compressed video sequences.Generally,there are many B-frames in each GOP.We utilize the B-frames to hide information so that we can increase the amount of hiding information and decrease the tamper of the carriers.Hiding and extracting are performed in compressed video sequences without requiring original video.The experimental results show that the proposed method has the characteristics of less tampering on the visual effect and larger hiding capacity without increasing the volume of the carrier file.

Motion Analysis for Human Interaction Detection Using Optical Flow on Lattice Superpixels

We develop a new video-based motion analysis algorithn to determine whether two persons have any interaction in their meet- ing. The interaction between two persons can be very general, such as shaking hands, exchanging objects, and so on. To make the motio~ analysis robust to image noise, we segment each video flame into a set of superpixels and then derive a motion feature and a motion pattern for each superpixel by averaging the optical flow within the superpixe Specifically, we use the lattice cut to construct the superpixels, which are spatially and temporally consistent across frames. Based on the motion feature and the motion pattern of the superpixels, we develop an algorithm to divide an input video sequence into three consecutive periods： 1） two persons walking toward each other, 2） two persons meeting each other, and 3） two persons walking away fi＇om each other. The experiment show that the proposed algorithm can accurately dis- tinguish the videos with and without human interactions.