@inproceedings{jannatus-saba-etal-2021-study,
title = "A Study on Using Semantic Word Associations to Predict the Success of a Novel",
author = "Jannatus Saba, Syeda and
Bijoy, Biddut Sarker and
Gorelick, Henry and
Ismail, Sabir and
Islam, Md Saiful and
Amin, Mohammad Ruhul",
editor = "Ku, Lun-Wei and
Nastase, Vivi and
Vuli{\'c}, Ivan",
booktitle = "Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.starsem-1.4",
doi = "10.18653/v1/2021.starsem-1.4",
pages = "38--51",
abstract = "Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget{'}s thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget{'}s thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget{'}s Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.",
}
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<abstract>Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget’s thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget’s thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget’s Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.</abstract>
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%0 Conference Proceedings
%T A Study on Using Semantic Word Associations to Predict the Success of a Novel
%A Jannatus Saba, Syeda
%A Bijoy, Biddut Sarker
%A Gorelick, Henry
%A Ismail, Sabir
%A Islam, Md Saiful
%A Amin, Mohammad Ruhul
%Y Ku, Lun-Wei
%Y Nastase, Vivi
%Y Vulić, Ivan
%S Proceedings of *SEM 2021: The Tenth Joint Conference on Lexical and Computational Semantics
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F jannatus-saba-etal-2021-study
%X Many new books get published every year, and only a fraction of them become popular among the readers. So the prediction of a book success can be a very useful parameter for publishers to make a reliable decision. This article presents the study of semantic word associations using the word embedding of book content for a set of Roget’s thesaurus concepts for book success prediction. In this work, we discuss the method to represent a book as a spectrum of concepts based on the association score between its content embedding and a global embedding (i.e. fastText) for a set of semantically linked word clusters. We show that the semantic word associations outperform the previous methods for book success prediction. In addition, we present that semantic word associations also provide better results than using features like the frequency of word groups in Roget’s thesaurus, LIWC (a popular tool for linguistic inquiry and word count), NRC (word association emotion lexicon), and part of speech (PoS). Our study reports that concept associations based on Roget’s Thesaurus using word embedding of individual novel resulted in the state-of-the-art performance of 0.89 average weighted F1-score for book success prediction. Finally, we present a set of dominant themes that contribute towards the popularity of a book for a specific genre.
%R 10.18653/v1/2021.starsem-1.4
%U https://aclanthology.org/2021.starsem-1.4
%U https://doi.org/10.18653/v1/2021.starsem-1.4
%P 38-51
Markdown (Informal)
[A Study on Using Semantic Word Associations to Predict the Success of a Novel](https://aclanthology.org/2021.starsem-1.4) (Jannatus Saba et al., *SEM 2021)
ACL