  Keywords perform a significant role in selecting various topic-related
documents quite easily. Topics or keywords assigned by humans or experts
provide accurate information. However, this practice is quite expensive in
terms of resources and time management. Hence, it is more satisfying to utilize
automated keyword extraction techniques. Nevertheless, before beginning the
automated process, it is necessary to check and confirm how similar
expert-provided and algorithm-generated keywords are. This paper presents an
experimental analysis of similarity scores of keywords generated by different
supervised and unsupervised automated keyword extraction algorithms with expert
provided keywords from the Electric Double Layer Capacitor (EDLC) domain. The
paper also analyses which texts provide better keywords like positive sentences
or all sentences of the document. From the unsupervised algorithms, YAKE,
TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction.
From the supervised algorithms, KEA and WINGNUS are employed for keyword
extraction. To assess the similarity of the extracted keywords with
expert-provided keywords, Jaccard, Cosine, and Cosine with word vector
similarity indexes are employed in this study. The experiment shows that the
MultipartiteRank keyword extraction technique measured with cosine with word
vector similarity index produces the best result with 92% similarity with
expert provided keywords. This study can help the NLP researchers working with
the EDLC domain or recommender systems to select more suitable keyword
extraction and similarity index calculation techniques.
