Recapitulization of Tweets Using Graph-based Clustering

被引:0
|
作者
Lobo, Vivian Brian [1 ]
Ansari, Nazneen [2 ]
机构
[1] St Francis Inst Technol, Dept Comp Engn, Mumbai 400103, Maharashtra, India
[2] St Francis Inst Technol, Dept Informat Technol, Mumbai 400103, Maharashtra, India
来源
2017 2ND INTERNATIONAL CONFERENCE ON COMMUNICATION SYSTEMS, COMPUTING AND IT APPLICATIONS (CSCITA) | 2017年
关键词
clustering; graphs; recapitulization; tweets; twitter;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Twitter, a well-liked online social networking site, facilitates millions of users on a daily basis to dispatch and orate quick 140-character notes named tweets. Nowadays, twitter is cogitated as the fastest and popular intermediate of communication and is used to follow latest events. Tweets pertaining to a specific event can be effortlessly found using keyword matching, but there are numerous tweets that are likely to contain information that is semantically identical. Moreover, there exist many systems for recapitulating tweets related to a particular event, but they have numerous limitations and are unable to provide accurate results. This work aims to overcome the limitations of existing systems by developing a system for recapitulating tweets using graph-based clustering.
引用
收藏
页码:101 / 106
页数:6
相关论文
共 50 条
  • [21] Low-rank kernel learning for graph-based clustering
    Kang, Zhao
    Wen, Liangjian
    Chen, Wenyu
    Xu, Zenglin
    KNOWLEDGE-BASED SYSTEMS, 2019, 163 : 510 - 517
  • [22] Graph-Based Clustering via Group Sparsity and Manifold Regularization
    Miao, Jianyu
    Yang, Tiejun
    Jin, Junwei
    Niu, Lingfeng
    IEEE ACCESS, 2019, 7 : 172123 - 172135
  • [23] A new graph-based clustering approach: Application to PMSI data
    Elghazel, Haytham
    Kheddouci, Hamamache
    Deslandres, Veronique
    Dussauchoy, Alain
    2006 INTERNATIONAL CONFERENCE ON SERVICE SYSTEMS AND SERVICE MANAGEMENT, VOLS 1 AND 2, PROCEEDINGS, 2006, : 110 - 115
  • [24] Assessment of Clustering Tendency through Progressive Random Sampling and Graph-Based Clustering Results
    Prasad, K. Rajendra
    Reddy, B. Eswara
    PROCEEDINGS OF THE 2013 3RD IEEE INTERNATIONAL ADVANCE COMPUTING CONFERENCE (IACC), 2013, : 726 - 731
  • [25] Fast Graph-Based Relaxed Clustering for Large Data Sets Using Minimal Enclosing Ball
    Qian, Pengjiang
    Chung, Fu-Lai
    Wang, Shitong
    Deng, Zhaohong
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 2012, 42 (03): : 672 - 687
  • [26] Graph-Based Clustering Approach for Economic and Financial Event Detection Using News Analytics Data
    Sidorov, Sergei P.
    Faizliev, Alexey R.
    Levshunov, Michael
    Chekmareva, Alfia
    Gudkov, Alexander
    Korobov, Eugene
    SOCIAL INFORMATICS (SOCINFO 2018), PT II, 2018, 11186 : 271 - 280
  • [27] Clustering based Personality Prediction on Turkish Tweets
    Tutaysalgir, Esen
    Karagoz, Pinar
    Toroslu, Ismail H.
    PROCEEDINGS OF THE 2019 IEEE/ACM INTERNATIONAL CONFERENCE ON ADVANCES IN SOCIAL NETWORKS ANALYSIS AND MINING (ASONAM 2019), 2019, : 825 - 828
  • [28] Bivariate Spatial Clustering Analysis of Point Patterns: A Graph-Based Approach
    Robertson, Colin
    Roberts, Steven
    COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2013, PT IV, 2013, 7974 : 403 - 418
  • [29] Graph-based unsupervised feature selection and multiview clustering for microarray data
    Swarnkar, Tripti
    Mitra, Pabitra
    JOURNAL OF BIOSCIENCES, 2015, 40 (04) : 755 - 767
  • [30] Graph-based unsupervised feature selection and multiview clustering for microarray data
    Tripti Swarnkar
    Pabitra Mitra
    Journal of Biosciences, 2015, 40 : 755 - 767