  Recent approaches to text analysis from social media and other corpora rely
on word lists to detect topics, measure meaning, or to select relevant
documents. These lists are often generated by applying computational lexicon
expansion methods to small, manually-curated sets of root words. Despite the
wide use of this approach, we still lack an exhaustive comparative analysis of
the performance of lexicon expansion methods and how they can be improved with
additional linguistic data. In this work, we present LEXpander, a method for
lexicon expansion that leverages novel data on colexification, i.e. semantic
networks connecting words based on shared concepts and translations to other
languages. We evaluate LEXpander in a benchmark including widely used methods
for lexicon expansion based on various word embedding models and synonym
networks. We find that LEXpander outperforms existing approaches in terms of
both precision and the trade-off between precision and recall of generated word
lists in a variety of tests. Our benchmark includes several linguistic
categories and sentiment variables in English and German. We also show that the
expanded word lists constitute a high-performing text analysis method in
application cases to various corpora. This way, LEXpander poses a systematic
automated solution to expand short lists of words into exhaustive and accurate
word lists that can closely approximate word lists generated by experts in
psychology and linguistics.
