When E-commerce Meets Social Media: Identifying Business on WeChat Moment Using Bilateral-Attention LSTM

Tianlang Chen, Yuxiao Chen, Han Guo, Jiebo Luo

Abstract: WeChat Business, developed on WeChat, the most extensively used instant messaging platform in China, is a new business model that bursts into people's lives in the e-commerce era. As one of the most typical WeChat Business behaviors, WeChat users can advertise products, advocate companies and share customer feedback to their WeChat friends by posting a WeChat Moment--a public status that contains images and a text. Given its popularity and significance, in this paper, we propose a novel Bilateral-Attention LSTM network (BiATT-LSTM) to identify WeChat Business Moments based on their texts and images. In particular, different from previous schemes that equally consider visual and textual modalities for a joint visual-textual classification task, we start our work with a text classification task based on an LSTM network, then we incorporate a bilateral-attention mechanism that can automatically learn two kinds of explicit attention weights for each word, namely 1) a global weight that is insensitive to the images in the same Moment with the word, and 2) a local weight that is sensitive to the images in the same Moment. In this process, we utilize visual information as a guidance to figure out the local weight of a word in a specific Moment. Two-level experiments demonstrate the effectiveness of our framework. It outperforms other schemes that jointly model visual and textual modalities. We also visualize the bilateral-attention mechanism to illustrate how this mechanism helps joint visual-textual classification.