Semrank: A Semantic Similarity-Based Tweets Ranking Approach

Semrank: A Semantic Similarity-Based Tweets Ranking Approach

Jagrati Singh, Anil Kumar Singh
Copyright: © 2021 |Volume: 15 |Issue: 3 |Pages: 23
ISSN: 1557-3958|EISSN: 1557-3966|EISBN13: 9781799859840|DOI: 10.4018/IJCINI.20210701.oa6
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MLA

Singh, Jagrati, and Anil Kumar Singh. "Semrank: A Semantic Similarity-Based Tweets Ranking Approach." IJCINI vol.15, no.3 2021: pp.74-96. http://doi.org/10.4018/IJCINI.20210701.oa6

APA

Singh, J. & Singh, A. K. (2021). Semrank: A Semantic Similarity-Based Tweets Ranking Approach. International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), 15(3), 74-96. http://doi.org/10.4018/IJCINI.20210701.oa6

Chicago

Singh, Jagrati, and Anil Kumar Singh. "Semrank: A Semantic Similarity-Based Tweets Ranking Approach," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI) 15, no.3: 74-96. http://doi.org/10.4018/IJCINI.20210701.oa6

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Abstract

Popular real-world events often create huge traffic on Twitter including real-time updates of important moments, personal comments, and so on while the event is happening. Most of the users are interested to read the important tweets that possibly include important moments of that event. However, extracting the relevant tweets of any event is a challenging task due to the endless stream of noisy tweets and vocabulary variation problem of social media content. To handle these challenges, the authors introduce a new approach for computing the relative tweet importance based on the concept of the Pagerank algorithm where adjacency matrix of the graph representation of tweets contains semantic similarity matrix based on the word mover's distance measure utilizing Word2Vec word embedding model. The results show that top-ranked tweets generated by the proposed approach are more concise and news-worthy than baseline approaches.