[vc_empty_space][vc_empty_space]
Music interest classification of twitter users using support vector machine
Yusraa, Fikry M.a, Trilaksono B.R.b, Yendra R.a, Fudholi A.c
a Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Sultan Syarif Kasim (UIN Suska), Pekanbaru, 28293, Indonesia
b School of Electrical and Informatics Engineering, Bandung Institute of Technology, Bandung, Indonesia
c Solar Energy Research Institute, Universiti Kebangsaan Malaysia, Bangi, 43600, Malaysia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2005 – ongoing JATIT & LLS.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%).[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Music genre category,Sentiment,SVM,Tweet[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]