Pub Date : 2023-01-31DOI: 10.48550/arXiv.2301.13507
Mengyisong Zhao, Morgan Harvey, D. Cameron, F. Hopfgartner, V. Gillet
Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a considerable challenge. Being able to understand what makes a given song a hit is clearly beneficial to the whole music industry. Previous approaches to hit song prediction have focused on using audio features of a record. This study aims to improve the prediction result of the top 10 hits among Billboard Hot 100 songs using more alternative metadata, including song audio features provided by Spotify, song lyrics, and novel metadata-based features (title topic, popularity continuity and genre class). Five machine learning approaches are applied, including: k-nearest neighbours, Naive Bayes, Random Forest, Logistic Regression and Multilayer Perceptron. Our results show that Random Forest (RF) and Logistic Regression (LR) with all features (including novel features, song audio features and lyrics features) outperforms other models, achieving 89.1% and 87.2% accuracy, and 0.91 and 0.93 AUC, respectively. Our findings also demonstrate the utility of our novel music metadata features, which contributed most to the models' discriminative performance.
热门歌曲预测是音乐信息检索(MIR)中的新兴领域之一，但仍然是一个相当大的挑战。能够理解是什么让一首歌曲成为热门歌曲，显然对整个音乐产业都是有益的。以前预测热门歌曲的方法主要是利用唱片的音频特征。本研究旨在使用更多可替代的元数据，包括Spotify提供的歌曲音频功能、歌词和新颖的基于元数据的功能(标题主题、流行度连续性和类型类别)，改善Billboard Hot 100歌曲前10名的预测结果。应用了五种机器学习方法，包括:k近邻、朴素贝叶斯、随机森林、逻辑回归和多层感知机。我们的研究结果表明，随机森林(RF)和逻辑回归(LR)的所有特征(包括小说特征、歌曲音频特征和歌词特征)优于其他模型，准确率分别达到89.1%和87.2%，AUC分别为0.91和0.93。我们的发现还证明了我们的新音乐元数据特征的实用性，这对模型的判别性能贡献最大。
{"title":"An Analysis of Classification Approaches for Hit Song Prediction using Engineered Metadata Features with Lyrics and Audio Features","authors":"Mengyisong Zhao, Morgan Harvey, D. Cameron, F. Hopfgartner, V. Gillet","doi":"10.48550/arXiv.2301.13507","DOIUrl":"https://doi.org/10.48550/arXiv.2301.13507","url":null,"abstract":"Hit song prediction, one of the emerging fields in music information retrieval (MIR), remains a considerable challenge. Being able to understand what makes a given song a hit is clearly beneficial to the whole music industry. Previous approaches to hit song prediction have focused on using audio features of a record. This study aims to improve the prediction result of the top 10 hits among Billboard Hot 100 songs using more alternative metadata, including song audio features provided by Spotify, song lyrics, and novel metadata-based features (title topic, popularity continuity and genre class). Five machine learning approaches are applied, including: k-nearest neighbours, Naive Bayes, Random Forest, Logistic Regression and Multilayer Perceptron. Our results show that Random Forest (RF) and Logistic Regression (LR) with all features (including novel features, song audio features and lyrics features) outperforms other models, achieving 89.1% and 87.2% accuracy, and 0.91 and 0.93 AUC, respectively. Our findings also demonstrate the utility of our novel music metadata features, which contributed most to the models' discriminative performance.","PeriodicalId":93543,"journal":{"name":"Diversity, divergence, dialogue : 16th international conference, iConference 2021, Beijing, China, March 17-31, 2021 : proceedings. iConference (Conference) (16th : 2021 : Online)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89735636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-28032-0_10
Shiting Fu, Tingting Jiang
{"title":"Motivations, Purposes, and Means of Creating Information Cocoons Intentionally for Oneself: Looking on the Bright Side","authors":"Shiting Fu, Tingting Jiang","doi":"10.1007/978-3-031-28032-0_10","DOIUrl":"https://doi.org/10.1007/978-3-031-28032-0_10","url":null,"abstract":"","PeriodicalId":93543,"journal":{"name":"Diversity, divergence, dialogue : 16th international conference, iConference 2021, Beijing, China, March 17-31, 2021 : proceedings. iConference (Conference) (16th : 2021 : Online)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76148371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-28035-1_22
Fatima Tabassum, Sameera Mubarak, Lin Liu, J. Du
{"title":"How Many Features Do We Need to Identify Bots on Twitter?","authors":"Fatima Tabassum, Sameera Mubarak, Lin Liu, J. Du","doi":"10.1007/978-3-031-28035-1_22","DOIUrl":"https://doi.org/10.1007/978-3-031-28035-1_22","url":null,"abstract":"","PeriodicalId":93543,"journal":{"name":"Diversity, divergence, dialogue : 16th international conference, iConference 2021, Beijing, China, March 17-31, 2021 : proceedings. iConference (Conference) (16th : 2021 : Online)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85473882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-28035-1_35
Hong Cui, Noah Giebink, J. Starr, Dylan Longert, Bruce Ford, Étienne Léveillé‐Bourret
{"title":"From Noisy Data to Useful Color Palettes: One Step in Making Biodiversity Data FAIR","authors":"Hong Cui, Noah Giebink, J. Starr, Dylan Longert, Bruce Ford, Étienne Léveillé‐Bourret","doi":"10.1007/978-3-031-28035-1_35","DOIUrl":"https://doi.org/10.1007/978-3-031-28035-1_35","url":null,"abstract":"","PeriodicalId":93543,"journal":{"name":"Diversity, divergence, dialogue : 16th international conference, iConference 2021, Beijing, China, March 17-31, 2021 : proceedings. iConference (Conference) (16th : 2021 : Online)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81484876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-28035-1_32
Elijah John F. Dar Juan
{"title":"The Design, Development, and Implementation of an LIS General Education Course for Non-lIS University Students in the Philippines","authors":"Elijah John F. Dar Juan","doi":"10.1007/978-3-031-28035-1_32","DOIUrl":"https://doi.org/10.1007/978-3-031-28035-1_32","url":null,"abstract":"","PeriodicalId":93543,"journal":{"name":"Diversity, divergence, dialogue : 16th international conference, iConference 2021, Beijing, China, March 17-31, 2021 : proceedings. iConference (Conference) (16th : 2021 : Online)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90768619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-28035-1_39
Minhyung Jo, S. Ho, G. Burnett
{"title":"Do You Speak Meme? A Dynamic Digital Language for the InformationSociety","authors":"Minhyung Jo, S. Ho, G. Burnett","doi":"10.1007/978-3-031-28035-1_39","DOIUrl":"https://doi.org/10.1007/978-3-031-28035-1_39","url":null,"abstract":"","PeriodicalId":93543,"journal":{"name":"Diversity, divergence, dialogue : 16th international conference, iConference 2021, Beijing, China, March 17-31, 2021 : proceedings. iConference (Conference) (16th : 2021 : Online)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90778211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1007/978-3-031-28035-1_3
Jennifer Douglas, Alexandra Alisauskas
{"title":"#Receipts: A Case Study of the Sonja Ahlers Archive as Platform Feminism","authors":"Jennifer Douglas, Alexandra Alisauskas","doi":"10.1007/978-3-031-28035-1_3","DOIUrl":"https://doi.org/10.1007/978-3-031-28035-1_3","url":null,"abstract":"","PeriodicalId":93543,"journal":{"name":"Diversity, divergence, dialogue : 16th international conference, iConference 2021, Beijing, China, March 17-31, 2021 : proceedings. iConference (Conference) (16th : 2021 : Online)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76068145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：