Word Embedding Based Event Detection on Social Media

Date

2017-06-23

Author

Ertugrul, Ali Mert
Velioglu, Burak
Karagöz, Pınar

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

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Event detection from social media messages is conventionally based on clustering the message contents. The most basic approach is representing messages in terms of term vectors that are constructed through traditional natural language processing (NLP) methods and then assigning weights to terms generally based on frequency. In this study, we use neural feature extraction approach and explore the performance of event detection under the use of word embeddings. Using a corpus of a set of tweets, message terms are embedded to continuous space. Message contents that are represented as vectors of word embedding are grouped by using hierarchical clustering. The technique is applied on a set of Twitter messages posted in Turkish. Experimental results show that automatically extracted features detect the contextual similarities between tweets better than traditional feature extraction with term frequency-inverse document frequency (TF-IDF) based term vectors.

Subject Keywords

Event detection, Neural feature extraction, Word embedding, Neural probabilistic language models

URI

https://hdl.handle.net/11511/34492

DOI

https://doi.org/10.1007/978-3-319-59650-1_1

Collections

Department of Computer Engineering, Conference / Seminar

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Citation Formats

A. M. Ertugrul, B. Velioglu, and P. Karagöz, “Word Embedding Based Event Detection on Social Media,” 2017, vol. 10334, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/34492.