2021/09/04 by Bencheng Wei, Wei, Bencheng
Computer Science · Physics and Astronomy · #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Complex Network Analysis Techniques #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining
paper · pdf · doi:10.48550/arxiv.2109.01765
openalex publication_date 2021/09/04 · openalex created_date 2021/09/13 · openalex updated_date 2026/07/28
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.