Cross-Lingual Propagation for Deep Sentiment Analysis

For many languages and domains, there is a paucity of available Given such valuable data, modern deep learning-based sentiment data and resources. In some cases, it may be challenging analysis methods excel at determining the sentiment to obtain sufficient in-domain training data, both because polarity of what is being said about companies, products, etc. there may be less data available online and because it may be (Wang et al. 2015). Unfortunately, such deep methods require somewhat harder to find annotators. Hence, a question that substantial amounts of training data, because multiple levels arises is whether one can assist deep networks by incorporating of computation, each with additional weights and parameters, external cues that enable the model to generalize better.