Abstract: In recent years,multi-modal sentiment analysis has become an increasingly popular research area,which extends traditional text-based sentiment analysis to a multi-modal level that combines text,images and sound.Multi-modal sentiment analysis usually requires the acquisition of independent information within a single modality and interactive information between different modalities.In order to use the context information of language expression in each modality to obtain these two kinds of information,a multi-modal sentiment analysis approach based on context-augmented LSTM was proposed.Specifically,each modality is encoded in combination with the context feature using LSTM which aims to capture the independent information within single modality firstly.Subsequently,the independent information of multi-modality is merged,and the other LSTM layer is utilized to obtain the interactive information between the different modalities to form a multi-modal feature representation.Finally,the max-pooling strategy is used to reduce the dimension of the multi-modal representation,which will be fed to the sentiment classifier.The method achieves 75.3% ACC on the MOSI data set and F1 reaches 74.9.Compared to traditional machine learning methods such as SVM,ACC is 8.1% higher and F1 is 7.3 higher.Compared with the current advanced deep learning method,it is 0.9% higher on ACC and 1.3 higher on F1.At the same time,the trainable parameters are reduced by about 20 times,and the training speed is increased by 10 times.The experimental results demonstrate that the performance of the proposed approach significantly outperforms the competitive multi-modal sentiment classification baselines.

Key words: Context enhancement, Multi-modal, Sentiment analysis