Using word embeddings in Twitter election classification

Yang, X., Macdonald, C. and Ounis, I. (2018) Using word embeddings in Twitter election classification. Information Retrieval, 21(2-3), pp. 183-207. (doi: 10.1007/s10791-017-9319-5)

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Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, as well as the parameters of the word embedding training process, namely the context window size, the dimensionality and the number of negative samples, on the attained classification performance. By comparing the classification results of word embedding models that have been trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data should align with the Twitter classification dataset both in data type and time period to achieve significantly better performance compared to baselines such as SVM with TF-IDF. Moreover, by evaluating the results of word embedding models trained using various context window sizes and dimensionalities, we find that large context window and dimension sizes are preferable to improve the performance. However, the number of negative samples parameter does not significantly affect the performance of the CNN classifiers. Our experimental results also show that choosing the correct word embedding model for use with CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings. Finally, for out-of-vocabulary (OOV) words that are not available in the learned word embedding models, we show that a simple OOV strategy to randomly initialise the OOV words without any prior knowledge is sufficient to attain a good classification performance among the current OOV strategies (e.g. a random initialisation using statistics of the pre-trained word embedding models).

Item Type:Articles
Glasgow Author(s) Enlighten ID:Macdonald, Professor Craig and Yang, Dr Xiao and Ounis, Professor Iadh
Authors: Yang, X., Macdonald, C., and Ounis, I.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Information Retrieval
ISSN (Online):1573-7659
Published Online:09 November 2017
Copyright Holders:Copyright © 2017 Springer Science+Business Media, LLC
First Published:First published in Information Retrieval 21(2-3):183-207
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
646621Explaining and Mitigating Electoral ViolenceSarah BirchEconomic and Social Research Council (ESRC)ES/L016435/1SPS - POLITICS