E-commerce has gone a long way in empowering merchants through the internet. In order to store the goods efficiently and arrange the marketing resource properly, it is important for them to make the accurate gross merchandise value (GMV) prediction. However, it's nontrivial to make accurate prediction with the deficiency of digitized data. In this article, we present a solution to better forecast GMV inside Alipay app. Thanks to graph neural networks (G NN) which has great ability to correlate different entities to enrich information, we propose Gaia, a graph neural network (GNN) model with temporal shift aware attention. Gaia leverages the relevant e-seller’ sales information and learn neighbor correlation based on temporal dependencies. By testing on Alipay's real dataset and comparing with other baselines, Gaia has shown the best performance. And Gaia is deployed in the simulated online environment, which also achieves great improvement compared with baselines.

Automatic extraction of new words is an indispensable precursor to many NLP tasks such as Chinese word segmentation, named entity extraction, and sentiment analysis.This paper aims at extracting new sentiment words from large-scale user-generated content.We propose a fully unsupervised, purely data-driven framework for this purpose.We design statistical measures respectively to quantify the utility of a lexical pattern and to measure the possibility of a word being a new word.The method is almost free of linguistic resources (except POS tags), and requires no elaborated linguistic rules.We also demonstrate how new sentiment word will benefit sentiment analysis.Experiment results demonstrate the effectiveness of the proposed method.