In today’s competitive business world, customer service is often at the heart of businesses that can help strengthen their brands. Resolution of customers’ complaints in a timely and efficient manner is key to improving customer satisfaction. Moreover, customers’ complaints play an important role in identifying their requirements which offer a starting point for effective and efficient planning of companies’ overall R&D and new product or service development activities. Having said that, organizations encounter challenges towards automatically identifying complaints buried deep in massive online content. Our current work centers around learning two closely related tasks, viz. complaint identification and sentiment classification. We leverage weak supervision to annotate the corpus with sentiment labels. We propose a deep multitask framework that features a knowledge element that uses AffectiveSpace to infuse commonsense knowledge specific features into the learning process. The framework models complaint identification (the primary task) and sentiment classification (supplementary task) simultaneously. Experimental results show that our proposed multitask system obtains the highest cross-validation accuracy of 83.73 +/- 1.52 % for the complaint identification task and 69.01 +/- 1.74 % for the sentiment classification task. Our proposed multitask system outperforms the single-task systems indicating a strong correlation between sentiment analysis and complaint classification tasks, thus benefiting from each other when learned concurrently.

在当今竞争激烈的商业世界中，客户服务通常是可以帮助增强品牌的企业的核心。及时有效地解决客户的投诉是提高客户满意度的关键。此外，客户的投诉在确定他们的要求方面起着重要作用，这些要求为有效而高效地计划公司的整体研发和新产品或服务开发活动提供了一个起点。话虽如此，组织在自动识别隐藏在大量在线内容中的投诉方面遇到了挑战。我们当前的工作围绕学习两个紧密相关的任务，即。投诉识别和情感分类。我们利用薄弱的监督为语料库加上情感标签。我们提出了一个深层的多任务框架，该框架具有一个知识元素，该知识元素使用AffectiveSpace将常识性的知识特定功能注入学习过程中。该框架同时对投诉识别（主要任务）和情感分类（补充任务）进行建模。实验结果表明，我们提出的多任务系统对投诉识别任务和情感分类任务的交叉验证准确性最高，分别为83.73 +/- 1.52％和69.01 +/- 1.74％。我们提出的多任务系统优于单任务系统，这表明情绪分析与投诉分类任务之间存在很强的相关性，