• DocumentCode
    260240
  • Title

    Online analyzing of texts in social network of Twitter

  • Author

    Minab, Shokoufeh Salem ; Jalali, Mehrdad

  • Author_Institution
    Sci. Soc. of Comput., Islamic Azad Univ., Mashhad, Iran
  • fYear
    2014
  • fDate
    26-27 Nov. 2014
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Appearing social networks these days, the capacity of produced information has an increasing growing. The usual learning techniques don´t have an efficient performance and the need of utilizing increasing learning methods is seen as a necessary factor. In mining the text in social networks we can see that text mining and social analyzing in texts are new topics in data analyzing which are considered as important factors growing very fast. Developing Microblogging sites like Twitter leads to make opportunities to make and applying some theories and technologies leading to mine and research trends. In this article we will evaluate Twitter the social network its characteristics and introducing and comparing data mining algorithms to online investigation on texting data. Researches show that stochastic gradient descent superior than other online evaluating techniques in analyzing text.
  • Keywords
    data mining; learning (artificial intelligence); social networking (online); text analysis; Twitter; data analysis; learning techniques; microblogging sites; online evaluation techniques; online text analysis; social network; stochastic gradient descent; text mining; Accuracy; Data mining; Data models; Prediction algorithms; Software; Twitter; Social Network Data stream; Text; Twitter;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Technology, Communication and Knowledge (ICTCK), 2014 International Congress on
  • Conference_Location
    Mashhad
  • Type

    conf

  • DOI
    10.1109/ICTCK.2014.7033533
  • Filename
    7033533