Affiliations: [a] School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, China
| [b] School of Computer Science, Beijing Institute of Technology, China
Correspondence:
[*]
Corresponding author: Waheed Yousuf Ramay, Ph.D. Scholar, School of Computer and Communication Engineering, University of Science and Technology Beijing, 30 xueyuan road Beijing, 100083 China. Tel.: +8615501163323; E-mail: [email protected].
Abstract: In the context of natural-language processing, keyword extraction has been studied widely. In promoting business-enterprise goods and services, however, a major challenge remains to extracting keywords effectively and efficiently from social-media user-generated data, wherein employed are traditional, language-dependent and supervised keyword-extraction techniques. This study contributes a keyword extraction analytic hierarchy process (KEAHP), as a language-independent and unsupervised keyword-extraction technique. By using four user-generated data attributes, KEAHP identifies keywords from the word co-occurrence in linguistic networks, based on a multiple-attribute decision-making approach. The proposed technique has been validated via a publically-available standard dataset, and the experimental results show the effectiveness and efficiency of the algorithm in KEAHP. Despite its limitations, the study contends that KEAHP can drastically improve performance in promoting business-enterprise goods and services, while also discussed are implications for future research and practice in keyword-extraction techniques.
Keywords: Analytic hierarchy process, social media data, keyword extraction, KEAHP, word co-occurrence network, event detection
