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
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;
Conference_Titel :
Technology, Communication and Knowledge (ICTCK), 2014 International Congress on
Conference_Location :
Mashhad
DOI :
10.1109/ICTCK.2014.7033533