Effective user intent mining with unsupervised word representation
  models and topic modelling
  Understanding the intent behind 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 data set, we train unsupervised
text representation models, and then develop an intent mapping model which
would rank the predefined intents base on cosine similarity between sentences
and intents. 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.
