Each sentence can be represented as an n×m input matrix, where n is the maximum number of words over all sentences (padding is performed to the length of the longest tweet) and m is the number of features (i.e., dimensionality of word embeddings).
this work is an intersection of (i) churn detection in social media, (ii) multilingual churn detection and (iii) churn detection in chatbots we present the related work for each domain sepa-rately.