Sentiment-aware Enhancements of PageRank-based Citation Metric, Impact Factor, and H-index for Ranking the Authors of Scholarly Articles - Gupta, Shikha; Kumar, Animesh (Computer Science, 2024)
Heretofore, the only way to evaluate an author has been frequency-based citation metrics. However, citations with a neutral sentiment possibly can not be considered in the same light as those expressing a positive or negative sentiment. We present sentiment-enhanced alternatives to three conventional metrics namely Impact Factor, H-index, and PageRank-based index. The proposal studies the impact of the proposed metrics on the ranking of authors. We experimented with two datasets, collectively comprising almost 20,000 citation sentences. The evaluation of the proposed metrics revealed a significant impact of sentiments on author ranking, evidenced by a weak Kendall coefficient for the Author Impact Factor and H-index. However, the PageRank-based metric showed a moderate to strong correlation, perhaps due to its prestige-based attributes. Furthermore, a remarkable Rank-biased deviation exceeding 28% was seen in all cases, indicating a stronger rank deviation in top-ordered ranks.