  Identification of important topics in a text can facilitate knowledge
curation, discover thematic trends, and predict future directions. In this
paper, we aim to quantitatively detect the most common research themes in the
emerging supercapacitor research area, and summarize their trends and
characteristics through the proposed unsupervised, machine learning approach.
We have retrieved the complete reference entries of article abstracts from
Scopus database for all original research articles from 2004 to 2021. Abstracts
were processed through a natural language processing pipeline and analyzed by a
latent Dirichlet allocation topic modeling algorithm for unsupervised topic
discovery. Nine major topics were further examined through topic-word
associations, Inter-topic distance map and topic-specific word cloud. We
observed the greatest importance is being given to performance metrics (28.2%),
flexible electronics (8%), and graphene-based nanocomposites (10.9%). The
analysis also points out crucial future research directions towards bio-derived
carbon nanomaterials (such as RGO) and flexible supercapacitors.
