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Trending Topics Detection Of Indonesian Tweets Using BN-grams And Doc-p

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Indra (Author)
مؤلفين آخرين: Winarko, Edi (Co-Author), Pulungan, Reza (Co-Author)
المجلد/العدد: مج31, ع2
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2019
الصفحات: 266 - 274
DOI: 10.33948/0584-031-002-011
ISSN: 1319-1578
رقم MD: 974637
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
كلمات المؤلف المفتاحية:
Trending Topics Detection | Twitter | BN-grams | Document Pivot
رابط المحتوى:
صورة الغلاف QR قانون
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LEADER 02403nam a22002537a 4500
001 1717374
024 |3 10.33948/0584-031-002-011 
041 |a eng 
044 |b السعودية 
100 |9 525825  |a Indra  |e Author 
245 |a Trending Topics Detection Of Indonesian Tweets Using BN-grams And Doc-p 
260 |b جامعة الملك سعود  |c 2019 
300 |a 266 - 274 
336 |a بحوث ومقالات  |b Article 
520 |b Researches on trending topics detection, especially on Twitter, have increased and various methods for detecting trending topics have been developed. Most of these researches have been focused on tweets written in English. Previous researches on trending topics detection on Indonesian tweets are still relatively few. In this paper, we compare two methods, namely document pivot and BN-grams, for detecting trending topics on Indonesian tweets. In our experiments, we examine the effects of varying the number of topics, n-grams, stemming, and aggregation on the quality of the resulting trending topics. We measure the accuracy of trending topics detection by comparing both algorithms with trending topics found in local news and Twitter trending topics. The results of our experiments show that using ten topics produces the highest topic recall; that using trigrams in BN-grams results in the highest value topic recall; and that using aggregation reduces the quality of trending topics produced. Overall, BN-grams has a higher value of topic recall than that of document pivot. © 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). 
653 |a وسائل الإعلام  |a شبكات التواصل الاجتماعي  |a اللغة الإندونيسية 
692 |b Trending Topics Detection  |b Twitter  |b BN-grams  |b Document Pivot 
700 |9 525827  |a Winarko, Edi  |e Co-Author 
700 |9 525829  |a Pulungan, Reza  |e Co-Author 
773 |c 011  |e Journal of King Saud University (Computer and Information Sciences)  |f Maǧalaẗ ǧamʼaẗ al-malīk Saud : ùlm al-ḥasib wa al-maʼlumat  |l 002  |m مج31, ع2  |o 0584  |s مجلة جامعة الملك سعود - علوم الحاسب والمعلومات  |v 031  |x 1319-1578 
856 |u 0584-031-002-011.pdf 
930 |d y  |p y 
995 |a science 
999 |c 974637  |d 974637 

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