# Global Local Fusion Neural Network for Multimodal Sentiment Analysis
## 2 Related Works  
### Unimodal Sentiment Analysis  
The research for text sentiment analysis has been developed for many years. There are multiple levels of text sentiment analysis based on the length of datasets, including document-level, sentence-level, and aspect-level sentiment analysis. The methods used to analyze these textual data can be divided into two categories, the lexicon-based approach and the machine learning approach. The lexicon-based technique is extremely feasible for sentence-level analysis. [PERSON] and [PERSON] [27] used a dictionary-based approach to build a thesaurus lexicon for sentiment tasks; the method adopted several dictionaries to collect a thesaurus and stored co-occurring words, which improved the classification accuracy without using human resources. [PERSON] et al. [28] first applied machine learning methods, Naive Bayes, and maximum entropy for movie review sentiment analysis. Moreover, some methods used a combination of lexicon and machine learning. [PERSON] and [PERSON] [29] first applied VADER with sentiment lexicon to provide the initial labels of emails and used LinearSVM to train the model and predict the sentiment of a not-yet-seen email, thereby preparing specific actions for customers who may have negative reactions. Recently, the deep neural network has been widely employed in natural language processing, including sentiment analysis. An attention-based bidirectional CNN-RNN model is proposed by [30], the model utilized two independent BiLSTM and GRU layers to capture past and future contexts in both directions, and an attention mechanism is applied to give weights to words for better performance.  
Visual sentiment analysis primarily aims to explore the emotions associated with images. The first paper on image sentiment analysis was published in 2010 [31]. The authors built the correlation between the sentiment of image and the visual content and performed a discriminative feature analysis to predict the image sentiment. [PERSON] et al. [32] proposed a large-scale visual sentiment ontology (VSO) based on psychological theories and web mining, which consists of many adjective-noun pairs (ANP), and then they trained the sentiment classifier with ANP outputs for the sentiment task. [PERSON] et al. [33] extracted emotions from the images by leveraging all the related information, such as visual features, comments, and friendships; the model can distinguish the comments closely related to images' emotional expression. Deep learning approaches have also been used in image sentiment analysis. [PERSON] et al. [34] introduced a convolutional neural network for the classification task. They performed fine-tuning training on the model and improved the results significantly. [PERSON] et al. [35] presented the network with an attention mechanism, which integrates visual attention into the CNN sentiment classification framework in an end-to-end manner. [PERSON] et al. [36] employed a model which fused salient object information in the image with complete image information to predict the users' sentiment. The model shows that reasonably utilizing the local information could improve the model's performance.