  The subject of conversational mining has become of great interest recently
due to the explosion of social and other online media. Supplementing this
explosion of text is the advancement in pre-trained language models which have
helped us to leverage these sources of information. An interesting domain to
analyse is conversations in terms of complexity and value. Complexity arises
due to the fact that a conversation can be asynchronous and can involve
multiple parties. It is also computationally intensive to process. We use
unsupervised methods in our work in order to develop a conversational pattern
mining technique which does not require time consuming, knowledge demanding and
resource intensive labelling exercises. The task of identifying repeating
patterns in sequences is well researched in the Bioinformatics field. In our
work, we adapt this to the field of Natural Language Processing and make
several extensions to a motif detection algorithm. In order to demonstrate the
application of the algorithm on a dynamic, real world data set; we extract
motifs from an open-source film script data source. We run an exploratory
investigation into the types of motifs we are able to mine.
