  Cross-domain sentiment analysis (CDSA) helps to address the problem of data
scarcity in scenarios where labelled data for a domain (known as the target
domain) is unavailable or insufficient. However, the decision to choose a
domain (known as the source domain) to leverage from is, at best, intuitive. In
this paper, we investigate text similarity metrics to facilitate source domain
selection for CDSA. We report results on 20 domains (all possible pairs) using
11 similarity metrics. Specifically, we compare CDSA performance with these
metrics for different domain-pairs to enable the selection of a suitable source
domain, given a target domain. These metrics include two novel metrics for
evaluating domain adaptability to help source domain selection of labelled data
and utilize word and sentence-based embeddings as metrics for unlabelled data.
The goal of our experiments is a recommendation chart that gives the K best
source domains for CDSA for a given target domain. We show that the best K
source domains returned by our similarity metrics have a precision of over 50%,
for varying values of K.
