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Luận văn thạc sĩ Khoa học máy tính: Dự báo chủ đề nóng trên mạng xã hội

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Tiêu đề Dự báo chủ đề nóng trên mạng xã hội
Tác giả Lê Văn Hùng
Người hướng dẫn GS. TS. Nguyễn Văn A, TS. Lê Thị B
Trường học Trường Đại học Khoa học Tự nhiên - Đại học Quốc gia Thành phố Hồ Chí Minh
Chuyên ngành Khoa học máy tính
Thể loại Luận văn thạc sĩ
Năm xuất bản 2019
Thành phố Tp. HCM
Định dạng
Số trang 44
Dung lượng 612,41 KB

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ASBTRACT

With the development of social networks, more and more information, topics are shared, discussed, and attracted a lot of users The problems of detecting, analyzing and predicting for hot topics are interested in research due to their high practical meaning in different application areas such as marketing and content promotion

Through studying the problem and related works, we have grasped and surveyed the hot topic prediction problem about the situation and challenges as well as the characteristics and predictive models used for the problem On that basis, we have proposed combining feature groups and developing methods to form positive and negative data samples for the problem of hot topic prediction Then we solve the problem as a binary classification problem with a machine learning approach, using supervised learning algorithms With the above suggestions for the dissertation, we have stated a problem, developed a solution, and conducted rigorous and complete evaluation experiments to create a comparative basis for the following works Experimental results were positive, improved with suggestions of the thesis, for the hot topic prediction problem

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DANH MӨC HÌNH

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DANH MӨC BҦNG BIӆU

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DANH MӨC TӮ VIӂT TҲT

API Application Programming Interface

GBDT Gradient Boosting Decision Tree

PAA Piecewise aggregate approximation

Trang 13

&KѭѫQJ GIӞI THIӊU

1.1 TӘNG QUAN

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1 https://zephoria.com/top-15-valuable-facebook-statistics/

2 http://www.internetlivestats.com/twitter-statistics/

Trang 14

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EiRYӅFiFFKӫÿӅQKѭSKiWKLӋQFKӫÿӅQyQJ hot topic detection GӵEiRFKӫÿӅQyQJ (hot topic prediction ÿѭӧFÿһWUDYjTXDQWkPQJKLrQFӭX

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WKӕQJ 7ZHYHQW [4] và Twitinfo [5] KѭӟQJ WLӃS FұQ GӵD WUrQ P{ KuQK KyD FKӫ ÿӅ

(Topic-modelling-based) [6]

1JѭӧFOҥLFiFF{QJWUuQKQJKLrQFӭXYӅGӵEiRFKӫÿӅQyQJOҥLFyQKLӅXNӃWTXҧUӡLUҥFWUrQQKLӅXEӝGӳOLӋXNKiFQKDXYjKҫXKӃWOjNK{QJÿѭӧFF{QJNKDL&iFNӃWTXҧQj\ NKLӃQ FKR FiF ÿӅ [XҩW KLӋQ WҥL cho bài WRiQ Gӵ EiR FKӫ ÿӅ QyQJ mang tính lý WKX\ӃWNKyVRViQK FK~QJW{LVӁWUuQKEj\NӻKѫQӣ&KѭѫQJ&{QJWUuQKOLrQTXDQ 

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1.2 MӨC TIÊU CӪ$Ĉӄ TÀI

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Trong Hình 1, KӋWKӕQJSKiWKLӋQYjGӵEiRFKӫÿӅQyQJWUrQPҥQJ[mKӝLÿѭӧFJLӟLWKLӋX 7URQJÿyQJKLrQFӭXKӋWKӕQJSKiWKLӋQFKӫÿӅFKӫÿӅQyQJWKѭӡQJOjEѭӟFÿҫXWLrQFKREjLWRiQGӵEiRFKӫÿӅQyQJ [7], [8] .KLQj\ÿҫXYjRFӫDEjLWRiQGӵEiRFKӫÿӅQyQJOjFiFEjLÿăQJFQJYӟLFiFGӳOLӋXNKiFFӫDFiFFKӫÿӅÿѭӧFSKiWKLӋQ ĈҫX UD VӁ Oj FiF GDQK ViFK FiF FKӫ ÿӅ ÿѭӧF Gӵ EiR Oj FKӫ ÿӅ QyQJ WURQJ các NKXQJWKӡLJLDQ WURQJWѭѫQJODL

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1.3 PHҤM VI CӪ$Ĉӄ TÀI

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Trang 16

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1.5 CҨU TRÚC LUҰ19Ă1

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x &KѭѫQJ4: 3KѭѫQJSKiSÿӅ[XҩW± 7UuQKEj\FiFSKѭѫQJSKiSÿӅ[XҩWFKR mô hình EjLWRiQGӵEiRFiFÿһFWUѭQJVӱGөQg, SKѭѫQJSKiS[k\GӵQJEӝGӳOLӋXcho bài toán GӵEiR

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&KѭѫQJ CÔNG TRÌNH LIÊN QUAN

Bài toán dӵ EiR FKӫ ÿӅ QyQJ YӟL QKLӅX WrQ JӑL NKiF QKDX QKѭ ³topic attention forecast´ ³trend detection´ ³popularity prediction´ 0өF WLrX FKtQK FӫD EjL WRiQ Gӵ

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WUѭQJ Oj Burst Time Prediction in Cascades [16], Emerging Product Topics Prediction in Social Media without Social Structure Information [17] Các công

WUuQKQj\ÿmVӱGөQJvà trình bày khá rõ FiFÿһFWUѭQJYӅFKXӛLWKӡLJLDQWәQJTXiWQKyP ÿһF WUѭQJ YӅ GDR ÿӝQJ YӅ QJѭӡL GQJ FNJQJ QKѭ FiF TXDQ KӋ JLӳD FiF QJѭӡL

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9Ӆ WұS Gӳ OLӋX ÿiQK JLi, KLӋQ WҥL FiF F{QJ WUuQK ÿDQJ Vӱ GөQJ NKi QKLӅX Eӝ Gӳ OLӋXkhác nhau QKѭQJ FK~QJ W{L YүQ FKѭD Fy NӃW TXҧ WuP NLӃP FiF Eӝ Gӳ OLӋX F{QJ NKDLEҵQJWLӃQJ$QK&iFÿӅWjLQәLEұWJҫQÿk\FKѭDF{QJNKDLFiFEӝGӳOLӋXFӫDPuQK[12] [15] MӝWVӕÿӅ WjLVӱGөQJFiFGӳOLӋXWLӃQJ7UXQJNKiFUҩWNKyWLӃSFұQ[16] [8]

3 'Rÿy trong OXұQYăQQj\ FK~QJW{LTX\ӃWÿӏQK[k\GӵQJEӝGӳOLӋXWӯEӝGӳOLӋXÿѭӧFVӱGөQJUӝQJUmLWURQJEjLWRiQSKiWKLӋQFKӫÿӅQyQJ(YHQW2012 QKѭÿmÿӅFұSӣWUrQ

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WUuQK WUѭӟF 0ӝW YjL NӃW TXҧ ÿѫQ Oҿ ÿiQJ FK~ ê QKѭ sau Ӣ công trình Burst Time Prediction in Cascades [16]NӃWTXҧGӵEiR ÿҥWFDRQKҩWOjYӟLÿӝÿR)QKѭQJ NKXQJWKӡLJLDQ GӵEiROjUӝQJ Ӣ công trình Realtime Online Hot Topics Prediction

in Sina Weibo for News Earlier Report [7] ÿѭD UD FiF NӃW TXҧ Gӵ EiR YjR NKRҧQJ

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bài toán Gӵ EiRFKӫÿӅQyQJÿѭӧFÿӅ[XҩWWURQJF{QJWUuQK When to Make a Topic Popular Again? A Temporal Model for Topic Re-hotting Prediction in Online Social Networks [12] ÿҥWNӃWTXҧGӵEiRFDRQKҩWYӟLÿӝÿR) Tuy nhiên, các

tác JLҧFKRUҵQJP{KuQKFӫDPuQKOjP{KuQKKӑFNK{QJJLiPViWNKXQJWKӡLJLDQ GӵEiRQJҳQYjGӵEiRÿѭӧFӣPӭFÿӏQKOѭӧQJ cho ÿӝQyQJFӫDFiFFKӫÿӅ

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3 https://aminer.org/influencelocality

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&KѭѫQJ PHÂN TÍCH VҨ1Ĉӄ

3.1 PHÁT BIӆU BÀI TOÁN DӴ BÁO CHӪ Ĉӄ NÓNG

Bài toán GӵEiRFKӫÿӅQyQJWUrQPҥQJ[mKӝL ÿѭӧFP{Wҧ QKѭVDX

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݂݅ݔ ൒  ݊௛ݐ݄݂݁݊ሺݔሻ ൌ ݐݎݑ݁Ǣ

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