Aiming at the problem that the cross-modality interaction and the impact of the contribution of each modality on the final sentiment classification results are not considered in multimodal sentiment analysis of video, a multimodal sentiment analysis model of Attention Mechanism based feature Fusion-Bidirectional Gated Recurrent Unit (AMF-BiGRU) was proposed. Firstly, Bidirectional Gated Recurrent Unit (BiGRU) was used to consider the interdependence between utterances in each modality and obtain the internal information of each modality. Secondly, through the cross-modality attention interaction network layer, the internal information of the modalities were combined with the interaction between modalities. Thirdly, an attention mechanism was introduced to determine the attention weight of each modality, and the features of the modalities were effectively fused together. Finally, the sentiment classification results were obtained through the fully connected layer and softmax layer. Experiments were conducted on open CMU-MOSI (CMU Multimodal Opinion-level Sentiment Intensity) and CMU-MOSEI (CMU Multimodal Opinion Sentiment and Emotion Intensity) datasets. The experimental results show that compared with traditional multimodal sentiment analysis methods (such as Multi-Attention Recurrent Network (MARN)), the AMF-BiGRU model has the accuracy and F1-Score on CMU-MOSI dataset improved by 6.01% and 6.52% respectively, and the accuracy and F1-Score on CMU-MOSEI dataset improved by 2.72% and 2.30% respectively. AMF-BiGRU model can effectively improve the performance of multimodal sentiment classification.

In order to solve the problem that the leakage of privacy content of images in the process of public channel transmission results in endangering information security, a new encryption method of greyscale image was proposed. The iteration of coupled logistic map was used to generate two-dimensional chaotic sequences. One of the sequences was used to generate the coefficients of cat map. The another was used to scramble the pixel positions. The traditional image encryption method based on cat map was used to encrypt the image pixel position, while the proposed encryption method was used to adopt different cat map coefficients for different pixel groups, so as to transform the grey value of each pixel in the group. In addition, bidirectional diffusion was adopted by the method to improve the security performance. The proposed method has simple encryption and decryption processes, high execution efficiency, and no limitation for the image size. Security analysis shows that the proposed encryption method is very sensitive to secret keys, and has good stability under multiple attack methods.

The data in internet social media has the characteristics of fast transmission, high user participation and complete coverage compared with traditional media under the background of the rise of various platforms on the internet.There are various topics that people pay attention to and publish comments in, and there may exist deeper and more fine-grained sub-topics in the related information of one topic. A survey of sub-topic detection based on internet social media, as a newly emerging and developing research field, was proposed. The method of obtaining topic and sub-topic information through social media and participating in the discussion is changing people’s lives in an all-round way. However, the technologies in this field are not mature at present, and the researches are still in the initial stage in China. Firstly, the development background and basic concept of the sub-topic detection in internet social media were described. Secondly, the sub-topic detection technologies were divided into seven categories, each of which was introduced, compared and summarized. Thirdly, the methods of sub-topic detection were divided into online and offline methods, and the two methods were compared, then the general technologies and the frequently used technologies of the two methods were listed. Finally, the current shortages and future development trends of this field were summarized.