In text clustering, sometimes the output of a k-means analysis may not be intuitive. This
function is designed to identify the main criteria of a cluster by identifying the top bi-gram
associated with each cluster.

This function creates a DataFrame of each cluster and their top bigram. It does this by
creating a subset of each cluster, and then creates bigrams for each individual row.
Those bigrams are joined together into a list, and then run through a counter to
identify the top bigram.

Parameters
--------------
dataset : DataFrame
    the dataframe being used as an input. At a minimum, it should have two fields: the cluster output and the text fed for clustering
cluster_loop : string
    the title of the field that records the different clusters output
row_loop : string
    the title of the field that includes the text for identifying the bigram (can be raw or preprocessed)
stopwords : bool (optional)
    if True, use stoplist to strip text

Returns
---------
top_gram :
    returns a DataFrame with 2 columns: the cluster title and a tuple including the topbigram and its count