Effective user intent mining with unsupervised word representation models and topic modelling

Understanding the intent behind email/chat between customers and customer service agents has become a crucial problem nowadays due to an exponential increase in the use of the Internet by people from different cultures and educational backgrounds. More importantly, the explosion of e-commerce has led to a significant increase in text conversation between customers and agents. In this paper, we propose an approach to data mining the conversation intents behind the textual data. Using the customer service dataset, we train unsupervised text representation models using continuous bag of words (CBOW) and Skip-Ngram, and then develop an intent mapping model which would rank the pre-defined intents base on cosine similarity between sentences' embeddings and intents' embeddings. Topic-modeling techniques are used to define intents and domain experts are also involved to interpret topic modelling results. With this approach, we can get a good understanding of the user intentions behind the unlabelled customer service textual data. NTRODUCTION Great amount of customer interactions such as call summaries, email requests, and meeting notes are generated daily by customer service agents.