  Citations play a vital role in understanding the impact of scientific
literature. Generally, citations are analyzed quantitatively whereas
qualitative analysis of citations can reveal deeper insights into the impact of
a scientific artifact in the community. Therefore, citation impact analysis
(which includes sentiment and intent classification) enables us to quantify the
quality of the citations which can eventually assist us in the estimation of
ranking and impact. The contribution of this paper is two-fold. First, we
benchmark the well-known language models like BERT and ALBERT along with
several popular networks for both tasks of sentiment and intent classification.
Second, we provide ImpactCite, which is XLNet-based method for citation impact
analysis. All evaluations are performed on a set of publicly available citation
analysis datasets. Evaluation results reveal that ImpactCite achieves a new
state-of-the-art performance for both citation intent and sentiment
classification by outperforming the existing approaches by 3.44% and 1.33% in
F1-score. Therefore, we emphasize ImpactCite (XLNet-based solution) for both
tasks to better understand the impact of a citation. Additional efforts have
been performed to come up with CSC-Clean corpus, which is a clean and reliable
dataset for citation sentiment classification.
