###### Abstract
To extract more concise and multivariate unimodal feature representations, the unaligned data fusion based on feature enhancement technology has been widely studied [16]. [PERSON] et al. [17] proposed a unimodal enhancement Transformer based on feature enhancement technology, which adds the extracted unimodal features to the unimodal features again to highlight the single peak information and solves the problem of text, video, and audio sentiment analysis under the unaligned data sequence scenarios. Nonetheless, they ignored the interaction between different modalities. [PERSON] et al. [18] proposed a Progressive Modal Enhancement (PMR) method by adding the message centers so that the modal enhancement unit and information updating unit complemented each other. Finally, the enhanced text, video, and audio fusion features are applied to sentiment analysis, and yet the model training complexity of this method increases. Hence, it is still challenging to explore the correlation and interaction between different modalities in the important time steps for cross-modal sentiment analysis in unaligned data sequence scenarios. To extract effective unimodal feature representations from long sequences, this paper proposes a sentiment analysis model (UA-BFET) based on feature enhancement technology for the cross-modal fusion of text and video in unaligned data scenarios, as shown in Figure 1. Firstly, the model inputs the obtained unimodal vector representation of text and video into a cyclic memory enhancement network across time steps (Cmenats). Secondly, the unimodal fusion features of adjacent time steps are obtained through Bi-GRU, and subsequently, the obtained unimodal fusion features of text and video are utilized as the inputs of the cross-modal hierarchical attention fusion module. Moreover, the obtained cross-modal fusion features with semantic interaction are applied to the unimodal feature extraction process of the next time step in the Bi-GRU system. Thus, the unimodal feature representations and cross-modal fusion feature representations are enhanced. The above operations can reduce the redundant information between the unaligned data sequences and extract effective unimodal feature representations. Moreover, the cross-modal hierarchical attention fusion module, text features, and video features obtained in adjacent time steps interact twice in layers to obtain the correlation and interaction between different modalities on important time steps. The cross-modal hierarchical attention fusion module focuses on the cross-modal interaction in the whole discourse scope, regardless of whether the different modalities in the unaligned data sequences need to be aligned. In the following, the enhanced cross-modal fusion features combined with the extracted text and video unimodal features are analyzed by canonical correlation analysis (CCA). The loss function of CCA can enhance the correlation between fusion features. Finally, the fusion features with enhanced correlation are input into the fully connected layer and the Softmax function for sentiment analysis. The main contributions of this paper are summarized as follows:  
* This paper proposes a sentiment analysis model (UA-BFET) based on feature enhancement technology in unaligned data scenarios. The model can be used to analyze the sentiment of unaligned text and video modality data in social media. * It uses a cyclic memory enhancement network across time steps and canonical correlation analysis (CCA) to extract effective unimodal feature representations and explore the correlation and interaction between different modalities. * The UA-BFET model proposed in this study reaches or even exceeds the sentiment analysis effect of text, video, and audio modalities fusion, which proves the feasibility of the proposed UA-BFET model. The rest of the paper is organized as follows: Section 2 describes the current related work of sentiment analysis of different modalities. Section 3 gives a description of the proposed UA-BFET sentiment analysis model.