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Predicting Music Genre Preferences Based on Online Comments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predicting Music Genre Preferences Based on Online Comments./
作者:	Sinclair, Andrew.
面頁冊數:	1 online resource (46 pages)
附註:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
標題:	Virtual communities. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30511596click for full text (PQDT)
ISBN:	9798379807702

Predicting Music Genre Preferences Based on Online Comments.
Sinclair, Andrew.

Predicting Music Genre Preferences Based on Online Comments. - 1 online resource (46 pages)

Source: Masters Abstracts International, Volume: 85-01.

Thesis (M.Sc.)--California Polytechnic State University, 2014.

Includes bibliographical references

Communication Accommodation Theory (CAT) states that individuals adapt to each other's communicative behaviors. This adaptation is called "convergence." In this work we explore the convergence of writing styles of users of the online music distribution platform SoundCloud.com. In order to evaluate our system we created a corpus of over 38,000 comments retrieved from SoundCloud in April 2014. The corpus represents comments from 8 distinct musical genres: Classical, Electronic, Hip Hop, Jazz, Country, Metal, Folk, and World. Our corpus contains: short comments, frequent misspellings, little sentence structure, hashtags, emoticons, and URLs. We adapt techniques used by researchers analyzing other short web-text corpora in order to deal with these problems. We use a supervised machine learning approach to classify the genre of comments in our corpus. We examine the effects of different feature sets and supervised machine learning algorithms on classification accuracy. In total we ran 180 experiments in which we varied: number of genres, feature set composition, and machine learning algorithm. In experiments with all 8 genres we achieve up to 40% accuracy using either a Naive Bayes classifier or C4.5 based classifier with a feature set consisting of 1262 token unigrams and bigrams. This represents a 3 time improvement over chance levels.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798379807702Subjects--Topical Terms:

3564377
Virtual communities.
Index Terms--Genre/Form:

542853
Electronic books.

Predicting Music Genre Preferences Based on Online Comments.
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504 $a Includes bibliographical references
520 $a Communication Accommodation Theory (CAT) states that individuals adapt to each other's communicative behaviors. This adaptation is called "convergence." In this work we explore the convergence of writing styles of users of the online music distribution platform SoundCloud.com. In order to evaluate our system we created a corpus of over 38,000 comments retrieved from SoundCloud in April 2014. The corpus represents comments from 8 distinct musical genres: Classical, Electronic, Hip Hop, Jazz, Country, Metal, Folk, and World. Our corpus contains: short comments, frequent misspellings, little sentence structure, hashtags, emoticons, and URLs. We adapt techniques used by researchers analyzing other short web-text corpora in order to deal with these problems. We use a supervised machine learning approach to classify the genre of comments in our corpus. We examine the effects of different feature sets and supervised machine learning algorithms on classification accuracy. In total we ran 180 experiments in which we varied: number of genres, feature set composition, and machine learning algorithm. In experiments with all 8 genres we achieve up to 40% accuracy using either a Naive Bayes classifier or C4.5 based classifier with a feature set consisting of 1262 token unigrams and bigrams. This represents a 3 time improvement over chance levels.
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