Tweet recommendation under user interest modeling with named entity recognition

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2014

Author

Karatay, Deniz

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Twitter has become one of the most important communication channels with its ability of providing the most up-to-date and newsworthy information. Considering wide use of Twitter as the source of information, reaching an interesting tweet for a user among a bunch of tweets is challenging. As a result of huge amount of tweets sent per day by hundred millions of users, information overload is inevitable. In order for users to reach the information that they are interested easily, recommendation of tweets is an essential task. To extract information from this large volume of tweets, Named Entity Recognition (NER), is already being used by researchers. Commonly used NER methods on formal texts such as newspaper articles are built upon on linguistic features extracted locally. However, considering the short and noisy nature of tweets, performance of these methods is inadequate on tweets and new approaches have to be generated to deal with this type of data. Recently, tweet representation based on segments in order to extract named entities has proven its validity in NER field. Along with named entities extracted from tweets via tweet segmentation, user’s retweet and mention history, and followed users are also considered as strong indicators of interest and a model representing user interest is generated. Reducing Twitter users’ effort to access tweets carrying the information of interest is the main goal of the study, and a tweet recommendation approach under a user interest model generated via named entities is presented.

Subject Keywords

Instant messaging., User-generated content., Entity-relationship modeling., Internet programming.

URI

http://etd.lib.metu.edu.tr/upload/12617569/index.pdf
https://hdl.handle.net/11511/23732

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

D. Karatay, “Tweet recommendation under user interest modeling with named entity recognition,” M.S. - Master of Science, Middle East Technical University, 2014.