  With the advent of social media, our online feeds increasingly consist of
short, informal, and unstructured text. This textual data can be analyzed for
the purpose of improving user recommendations and detecting trends. Instagram
is one of the largest social media platforms, containing both text and images.
However, most of the prior research on text processing in social media is
focused on analyzing Twitter data, and little attention has been paid to text
mining of Instagram data. Moreover, many text mining methods rely on annotated
training data, which in practice is both difficult and expensive to obtain. In
this paper, we present methods for unsupervised mining of fashion attributes
from Instagram text, which can enable a new kind of user recommendation in the
fashion domain. In this context, we analyze a corpora of Instagram posts from
the fashion domain, introduce a system for extracting fashion attributes from
Instagram, and train a deep clothing classifier with weak supervision to
classify Instagram posts based on the associated text.
  With our experiments, we confirm that word embeddings are a useful asset for
information extraction. Experimental results show that information extraction
using word embeddings outperforms a baseline that uses Levenshtein distance.
The results also show the benefit of combining weak supervision signals using
generative models instead of majority voting. Using weak supervision and
generative modeling, an F1 score of 0.61 is achieved on the task of classifying
the image contents of Instagram posts based solely on the associated text,
which is on level with human performance. Finally, our empirical study provides
one of the few available studies on Instagram text and shows that the text is
noisy, that the text distribution exhibits the long-tail phenomenon, and that
comment sections on Instagram are multi-lingual.
