Ranking News and Categorizing Based on User Interest and Various Factors

Authors

  • Pooja Walunj Computer Engineering, Sinhgad Academy of Engineering, Kondhwa
  • Srushti Yewale Computer Engineering, Sinhgad Academy of Engineering, Kondhwa
  • Anushka Sonawane Computer Engineering, Sinhgad Academy of Engineering, Kondhwa
  • Nita Sahane Computer Engineering, Sinhgad Academy of Engineering, Kondhwa
  • Prof. Gauri Bhange Computer Engineering, Sinhgad Academy of Engineering, Kondhwa

Keywords:

Information filtering, social computing, social network analysis, topic identification, topic ranking

Abstract

Now Days Important information from online sources has becomes a prominent research. The public of
daily events has been provided by mass media sources, mainly the news media, have usually informed us of daily
events. Today, online social media services such as Twitter contributing large amount of user generated data, which
have great importance to contain informative news-related content. For these resources to be useful, we must find a
way to filter noise and only capture the content that, based on its similarity to the news related data, however after
noise is removed, the overload data still exists in the remaining data so, we need to prioritize it for consumption. For
this, we can use three factors. we proposed an unsupervised method named as sociRank- which identifies news topic
widespread in social media as well as the news media, and after that ranks them by using MF, UA, AND UI as
relevance factors. First, the temporal prevalence of a topic (MF) of a topic. After that we are going to categorize all
news location wise based of reviews or comments.
The system will first use twitters dataset and filter out all the twits that are related to news. After filtration of news
related twits, news topics are ranked based on factors like MF, UA, UI . In addition, the system will also focus on
classifying news topics based on the Domain and location of user. It is expected that through the providing of filtered
news, instead of reading unnecessary data user gets to read quality news depending on his interests and location.

Published

2018-11-25

How to Cite

Pooja Walunj, Srushti Yewale, Anushka Sonawane, Nita Sahane, & Prof. Gauri Bhange. (2018). Ranking News and Categorizing Based on User Interest and Various Factors. International Journal of Advance Research in Engineering, Science & Technology, 5(11), 1–5. Retrieved from https://ijarest.org/index.php/ijarest/article/view/1864