Building a Semantic Recommendation Engine for News Feeds based on Emerging Topics from Tweets

Mihai Tabara, Mihai Dascalu, Stefan Trausan-Matu

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

The rise of social networks powered by the emergence of Web 2.0 unleashed a massive amount of generated user content. Concurrently with technology enhancements that facilitated its widespread, Web 2.0 became the engine which hastened the appearance of worldwide mass communication techniques. Alongside its advent, textual analysis changed as new user-centered content failed to comply with traditional grammar ruling. In this paper, we approach the problem of topic extraction from Twitter in the context of designing a recommendation engine to best matching user profiles to news feed articles. We propose a strategy to extract the concepts by means of Natural Language Processing and use of the semantic cohesion measurements to leverage the matching process. In order to prove the adequacy of our method, we have conducted a medium-scale evaluation. Our results demonstrate the particularities of the Twitter textual corpora, as well as how it can be used to infer geo-locations for its users.
Original languageEnglish
Title of host publication15th Int. Conf. on Networking in Education and Research (RoEduNet). Bucharest, Romania
Place of PublicationBucharest
PublisherIEEE
DOIs
Publication statusPublished - 1 Jan 2017
Externally publishedYes
Event15th Int. Conf. on Networking in Education and Research (RoEduNet) - Bucharest, Romania
Duration: 7 Sep 20169 Sep 2016
https://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7733872

Conference

Conference15th Int. Conf. on Networking in Education and Research (RoEduNet)
CountryRomania
CityBucharest
Period7/09/169/09/16
Internet address

Keywords

  • topic extraction
  • news recommendation
  • tweets
  • prediction of news feeds

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    Cite this

    Tabara, M., Dascalu, M., & Trausan-Matu, S. (2017). Building a Semantic Recommendation Engine for News Feeds based on Emerging Topics from Tweets. In 15th Int. Conf. on Networking in Education and Research (RoEduNet). Bucharest, Romania IEEE. https://doi.org/10.1109/RoEduNet.2016.7753209