User Interest Modeling in Twitter with Named Entity Recognition

Date

2015-05-18

Author

Karatay, Deniz
Karagöz, Pınar

Metadata

Show full item record

Item Usage Stats

111
views

0
downloads

Considering wide use of Twitter as the source of information, reaching an interesting tweet for a user among a bunch of tweets is challenging. In this work we propose a Named Entity Recognition (NER) based user profile modeling for Twitter users and employ this model to generate personalized tweet recommendations. Effectiveness of the proposed method is shown through a set of experiments. Copyright © 2015 held by author(s).

Subject Keywords

Named Entity Recognition, Tweet Ranking

URI

https://hdl.handle.net/11511/86426
https://www.scopus.com/record/display.uri?eid=2-s2.0-84938517055&origin=resultslist&sort=plf-f&src=s&st1=&st2=&sid=48e5fe9385decb5dbe06609ef43e9c26&sot=b&sdt=b&sl=79&s=TITLE-ABS-KEY+%28User+Interest+Modeling+in+Twitter+with+Named+Entity+Recognition%29&relpos=0&citeCnt=3&searchTerm=

Conference Name

International Conference on the World Wide Web, WWW 2015; 18 May 2015 through

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Developing a twitter bot that can join a discussion using state-of-the-art architectures Çetinkaya, Yusuf Mücahit; Toroslu, İsmail Hakkı; Department of Computer Engineering (2019) Twitter is today mostly used for sharing and commenting about news. In this manner, the interaction between Twitter users is inevitable. This interaction sometimes causes people to move daily debates to this social platform. Since being dominant in these debates is crucial, automation of this process becomes highly popular. In this work, we aim to train a bot that classifies posted tweets according to their semantic and generates logical tweets about a popular discussion, namely gun debate of the U.S. for t...
Utilizing Word Embeddings for Result Diversification in Tweet Search Onal, Kezban Dilek; Altıngövde, İsmail Sengör; Karagöz, Pınar (2015-12-04) The performance of result diversification for tweet search suffers from the well-known vocabulary mismatch problem, as tweets are too short and usually informal. As a remedy, we propose to adopt a query and tweet expansion strategy that utilizes automatically-generated word embeddings. Our experiments using state-of-the-art diversification methods on the Tweets2013 corpus reveal encouraging results for expanding queries and/or tweets based on the word embeddings to improve the diversification performance in...
Result Diversification for Tweet Search Ozsoy, Makbule Gulcin; Onal, Kezban Dilek; Altıngövde, İsmail Sengör (2014-10-14) Being one of the most popular microblogging platforms, Twitter handles more than two billion queries per day. Given the users' desire for fresh and novel content but their reluctance to submit long and descriptive queries, there is an inevitable need for generating diversified search results to cover different aspects of a query topic. In this paper, we address diversification of results in tweet search by adopting several methods from the text summarization and web search domains. We provide an exhaustive ...
Twitter use by politicians during social uprisings an analysis of Gezi park protests in Turkey KARKIN, NACİ; Yavuz, Nilay; PARLAK, İSMET; İKİZ, ÖZLEM ÖZDEŞİM (2015-06-30) Social uprisings clearly show that social media tools, especially Twitter, help news spread more than the press does recently. In some cases Twitter substitutes traditional media if censorship is enlarged to such a level that the mainstream media channels prefer not to reflect the actual volume of the protests. Twitter is also utilized by politicians during such events to reinforce "us vs. them" division, and to gain support and legitimization for their own actions. Using critical discourse analysis, this p...
Context Based Semantic Relations in Tweets Özer, Özdikiş; Karagöz, Pınar; Oğuztüzün, Mehmet Halit Seyfullah (Springer, 2014-01-01) Twitter, a popular social networking platform, provides a medium for people to share information and opinions with their followers. In such a medium, a flash event finds an immediate response. However, one concept may be expressed in many different ways. Because of users’ different writing conventions, acronym usages, language differences, and spelling mistakes, there may be variations in the content of postings even if they are about the same event. Analyzing semantic relationships and detecting these vari...

Citation Formats

D. Karatay and P. Karagöz, “User Interest Modeling in Twitter with Named Entity Recognition,” Florence; Italy, 2015, p. 17, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/86426.