We present methods for transforming raw dialogue data into a dataset suitable for processing with statistical NLP models. We reveal the potential pitfalls for processing this type of data, such as ensuring the representatives of the sample, the generalization ability of models, and the definition of the local context of the utterances. We use novel methods to solve these problems and demonstrate their effectiveness on an utterance classification problem. As a result, this paper provides guidelines for generating valuable datasets from dialogue data.