Toponym Recognition in Social Media for Estimating the Location of Events

Date

2015-11-17

Author

Sagcan, Meryem
Karagöz, Pınar

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

140
views

0
downloads

Prominence of social media such as Twitter and Facebook led to a huge collection of data over which event detection provides useful results. An important dimension of event detection is location estimation for detected events. Social media provides a variety of clues for location, such as geographical annotation from smart devices, location field in the user profile and the content of the message. Among these clues, message content needs more effort for processing, yet it is generally more informative. In this paper, we focus on extraction of location names, i.e., toponym recognition, from social media messages. We propose a a hybrid system, which uses both rule based and machine learning based techniques to extract toponyms from tweets. Conditional Random Fields (CRF) is used as the machine learning tool and features such as Part-of-Speech tags and conjunction window are defined in order to construct a CRF model for toponym recognition. In the rule based part, regular expressions are used in order to define some of the toponym recognition patterns as well as to provide a simple level of normalization in order to handle the informality in the text. Experimental results show that the proposed method has higher toponym recognition ratio in comparison to the previous studies.

Subject Keywords

Toponym extraction, Social media, Location estimation, Event

URI

https://hdl.handle.net/11511/46442

DOI

https://doi.org/10.1109/icdmw.2015.167

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

TOPONYM RECOGNITION ON TURKISH TWEETS Onal, Kezban Dilek; Karagöz, Pınar; Çakıcı, Ruket (2014-04-25) In recent years, Twitter has become a popular platform for following and spreading trends, news and ideas all over the world. Geographical scope of tweets is crucial to many tasks like disaster management, event tracking and information retrieval. First step for assigning a geographical location to a tweet is toponym recognition. Toponym Recognition (Geoparsing) is identification of toponyms (place names) in a text. In this study, we investigated performance of three existing approaches for toponym recognit...
Collective classification of user emotions in twitter İleri, İbrahim; Karagöz, Pınar; Department of Computer Engineering (2015) The recent explosion of social networks has generated a big amount of data including user opinions about varied subjects. For classifying the sentiment of user postings, many text-based techniques have been proposed in the literature. As a continuation of sentiment analysis, there are also studies on the emotion analysis. Because of the fact that many different emotions are needed to be dealt with at this point, the problem becomes much more complicated. In this thesis, a different user-centric approach is ...
Event Detection via Tracking the Change in Community Structure and Communication Trends Aktunc, Riza; Karagöz, Pınar; Toroslu, Ismail Hakki (2022-01-01) Event detection is a popular research problem aiming to detect events from various data sources, such as news texts, social media postings or social interaction patterns. In this work, event detection is studied on social interaction and communication data via tracking changes in community structure and communication trends. With this aim, various community structure and communication trend based event detection methods are proposed. Additionally, a new strategy called community size range based change trac...
Real-Time Lexicon-Based Sentiment Analysis Experiments On Twitter With A Mild (More Information, Less Data) Arslan, Yusuf; Birtürk, Ayşe Nur; Djumabaev, Bekjan; Kucuk, Dilek (2017-12-14) Sentiment analysis of Twitter data is a well studied area, however, there is a need for exploring the effectiveness of real-time approaches on small data sets that only include popular and targeted tweets. In this paper, we have employed several sentiment analysis techniques by using dynamic dictionaries and models, and performed some experiments on limited but relevant datasets to understand the popularity of some terms and the opinion of users about them. The results of our experiments are promising.
Context aware friend recommendation for location based social networks using random walk Bağcı, Hakan; Karagöz, Pınar (null; 2016-04-10) The location-based social networks (LBSN) facilitate users to check-in their current location and share it with other users. The accumulated check-in data can be employed for the benefit of users by providing personalized recommendations. In this paper, we propose a random walk based context-aware friend recommendation algorithm (RWCFR). RWCFR considers the current context (i.e. current social relations, personal preferences and current location) of the user to provide personalized recommendations. Our LBSN...

Citation Formats

M. Sagcan and P. Karagöz, “Toponym Recognition in Social Media for Estimating the Location of Events,” 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/46442.