User and Topic Hybrid Context Embedding for FinanceRelated Text Data Mining As large amounts of financerelated microblogs, posted by both professional and amateur investors, are mingling in online social networks that could bring impact to real financial markets, itu0027s an essential task to analyze financial texts posted by different users. Besides that, as barely nobody could be an allround expert in various financial products such as stocks, futures and bonds, topic preferences of different users should also be considered in these mining tasks. That is to say, financial text contents should be analyzed in both user and topic level. In recent advances of deep learning technology, learning the embedding representation of contents is a stateofart method in financial text mining over handcrafted feature extraction techniques. Along this line, in this paper we propose a hybrid embedding method that both the embedding representation of users and the embedding representation of financial topics are learned from the posted financerelated contents in online social networks. Following the commonly used methods in embeddingrelated researches, we apply extensive experiments with realworld datasets to show the effectiveness of our hybrid user and topic embedding (UTE) approach, in both intrinsic and extrinsic ways. Itu0027s shown that our approach can intrinsically distinguish social network users with their learned embeddings, so they can be grouped into humanexplainable clusters. Moreover, we also propose a case study on sentiment analysis by applying our user and topic hybrid embedding with a deep contextual neural network architecture. The results prove that our approach outperforms other baseline methods in financialrelated sentiment analysis, and hopefully for other downstream text mining tasks as well.

Learning Short Text Representation using NonNegative Matrix Factorization and Word Semantic Correlations With the emergence of online medias, short texts have been increasingly available. Because the short texts have a limited contextual information, they are sparse, noisy and ambiguous. Conventional models to text representation are hence not applicable. In this paper, we propose a representation learning model for short texts to tackle this challenge. Our proposed model is based on NonNegative Matrix Factorization (NNMF). Due to the problem lacking word cooccurrences information, it effectively learns the representation model by incorporating the semantic relationship between words generated by unsupervised learning methods. Finally, the NNMFbased model is effectively solved by using a gradient descentbased algorithm. Experimental results on two benchmark data sets, including sentiment140 and 20newsgroups, demonstrate that our proposed approach achieved the improvement of classification accuracy to stateoftheart topic discovery techniques.