Automatically associating social media posts with topics is an important prerequisite for effective search and recommendation on many social media platforms. However, topic classification of such posts is quite challenging because of (a) a large topic space (b) short text with weak topical cues, and (c) multiple topic associations per post. In contrast to most prior work which only focuses on post classification into a small number of topics ($10$-$20$), we consider the task of large-scale topic classification in the context of Twitter where the topic space is $10$ times larger with potentially multiple topic associations per Tweet. We address the challenges above by proposing a novel neural model, CTM that (a) supports a large topic space of $300$ topics and (b) takes a holistic approach to tweet content modeling -- leveraging multi-modal content, author context, and deeper semantic cues in the Tweet. Our method offers an effective way to classify Tweets into topics at scale by yielding superior performance to other approaches (a relative lift of $\mathbf{20}\%$ in median average precision score) and has been successfully deployed in production at Twitter.
翻译:将社交媒体职位与专题自动挂钩是有效搜索和推荐许多社交媒体平台的重要先决条件,然而,此类职位的专题分类具有相当大的挑战性,因为(a) 主题空间大(b) 专题提示薄弱的短文本,以及(c) 每个职位的多个主题关联。与大多数先前的工作相比,仅侧重于将职位分类成少数专题(10美元-20美元),我们认为,在Twitter背景下,大规模专题分类的任务,其专题空间比每个Tweet的潜在多个主题协会大10美元。 我们通过提出一个新的神经模型应对上述挑战,CTM(a) 支持一个300美元专题的大型专题空间,(b) 采用整体方法在Twitter上制作内容模型 -- -- 利用多模式内容、作者背景和更深的语义提示。我们的方法提供了一种有效的方法,通过向其他方法提供优异的绩效,将Tweets分类成专题(平均精准分数为$\mathb{20 ⁇ )。