Music interest classification of twitter users using support vector machine

Yusra, Muhammad Fikry, Bambang Riyanto Trilaksono, Rado Yendra, Ahmad Fudholi

Research output: Contribution to journalArticle

Abstract

Interest determination in music is very beneficial for business communities such as social network advertiser, music studio rental, musical instrument sales, and music concert promoter. This research discusses the classification of music interest of Twitter users based on their tweets in Bahasa Indonesia (Indonesian language). We classify tweets into three music genre categories (jazz, pop, or rock) and three sentiments (positive, negative, or neutral) using Support Vector Machine (SVM). Tweet text classification includes user text, retweet text, mention, hashtag, emoticon, and link (URL). Preprocessing is initiated with word segmentation, removal of symbol and numeric character codes, stemming, word normalization, removal of stopwords, and searching DBpedia for some important words that does not have basic words. This research use dataset of 450 tweets. By generating SVM model on training process that use 360 tweets, Gaussian RBF kernel, and 10-fold cross validation, a pair of parameter (C=0.7, γ=0.9) for music genre category and a pair of parameter (C=0.7, γ=0.8) for sentiment were obtained. The testing process use 90 tweets and resulting the best accuracy for music genre category (96.67%) and the best accuracy for sentiment (86.67%).

Original languageEnglish
Pages (from-to)2352-2358
Number of pages7
JournalJournal of Theoretical and Applied Information Technology
Volume95
Issue number11
Publication statusPublished - 2017

Fingerprint

Music
Support vector machines
Support Vector Machine
Musical instruments
Studios
Websites
Sales
Rocks
Testing
Industry
Text Classification
Numerics
Promoter
Cross-validation
Social Networks
Normalization
Preprocessing
Fold
Segmentation
Classify

Keywords

  • Music genre category
  • Sentiment
  • SVM
  • Tweet

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Music interest classification of twitter users using support vector machine. / Yusra; Fikry, Muhammad; Trilaksono, Bambang Riyanto; Yendra, Rado; Fudholi, Ahmad.

In: Journal of Theoretical and Applied Information Technology, Vol. 95, No. 11, 2017, p. 2352-2358.

Research output: Contribution to journalArticle

Yusra ; Fikry, Muhammad ; Trilaksono, Bambang Riyanto ; Yendra, Rado ; Fudholi, Ahmad. / Music interest classification of twitter users using support vector machine. In: Journal of Theoretical and Applied Information Technology. 2017 ; Vol. 95, No. 11. pp. 2352-2358.
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