Vector representations of words, also known as word embeddings, can be obtained through unsupervised learning on a large text corpus so that certain linguistic regularities and patterns are encoded. Unfortunately, the escort ads contain a plethora of emojis, acronyms, and (sometimes deliberate) typographical errors that are not encountered in more standard text data, which suggests that it is likely better to learn word embeddings from scratch on a large collection of escort ads instead of using previously published embeddings<cite> (Tong et al., 2017)</cite> .