###### Abstract  
Cross-modal sentiment analysis is an emerging research area in natural language processing. The core task of cross-modal fusion lies in cross-modal relationship extraction and joint feature learning. The existing research methods of cross-modal sentiment analysis focus on static text, video, audio, and other modality data but ignore the fact that different modality data are often unaligned in practical applications. There is a long-term time dependence among unaligned data sequences, and it is difficult to explore the interaction between different modalities. The paper proposes a sentiment analysis model (UA-BEFT) based on feature enhancement technology in unaligned data scenarios, which can perform sentiment analysis on unaligned text and video modality data in social media. Firstly, the model adds a cyclic memory enhancement network across time steps. Then, the obtained cross-modal fusion features with interaction are applied to the unimodal feature extraction process of the next time step in the Bi-directional Gated Recurrent Unit (Bi-GRU) so that the progressively enhanced unimodal features and cross-modal fusion features continuously complement each other. Secondly, the extracted unimodal text and video features taken jointly from the enhanced cross-modal fusion features are subjected to canonical correlation analysis (CCA) and input into the fully connected layer and Softmax function for sentiment analysis. Through experiments executed on unaligned public datasets MOSI and MOSEI, the UA-BEFT model has achieved or even exceeded the sentiment analysis effect of text, video, and audio modality fusion and has outstanding advantages in solving cross-modal sentiment analysis in unaligned data scenarios. cross-modal fusion; gated recurrent unit (GRU) network; multi-head attention; canonical correlation analysis; sentiment analysis +
Footnote †: journal: Medical  
are frequently unaligned. Although unaligned data sequences have the characteristic of longer length and contain richer information, they also contain a large amount of redundant information. Additionally, there is an asynchronous feature between different modalities, such as the inconsistent appearance of negative words in the text and sad facial expressions in the video, and this asynchronism results in ineffective integration between different modalities. All these problems increase the difficulty of sentiment analysis based on different modalities. Therefore, the motivation of this paper is to obtain more emotional feature representations from unaligned data, to find the correlation and interaction between text and video, and to better achieve emotional analysis. For the data fusion problem of different modalities in the scenarios of unaligned data sequences, previous studies typically adopted the method of forced word alignment before training the model [4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. For the three modalities of text, video, and audio, the pronunciation times of different words in the text are first obtained, and then the video and audio features are forcibly aligned with the pronunciation time intervals of the text. Nevertheless, enforcing word alignment often involves information in relation to the application areas and requires detailed information about the datasets, so it is challenging to implement. Subsequently, [PERSON] et al. [14] introduced neural machine translation (NMT) as a powerful inspiration and proposed a Multimodal Transformer (MulT) method to fuse the cross-modal information of unaligned data sequences. MulT solves the problem of cross-modal fusion under unaligned data sequences without explicitly aligning the modality data. [PERSON] et al. [15] designed a new lightweight network that introduced a Transformer with added cross-modal blocks to realize complementary learning of different modalities. Notwithstanding, the above studies ignored the importance of unimodal features in the cross-modal fusion of unaligned data sequences.