Multitask learning for complaint identification and sentiment analysis

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.