Link Prediction on Tweets’ Content

Martinčić-Ipšić, Sanda; Močibob, Edvin; Meštrović, Ana

doi:10.1007/978-3-319-46254-7_45

Cited by 4 publications

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“…Note however, that this comes at the cost of the high computation complexity of the procedures for calculating the properties. The GOW model in its diverse variants has been applied to many natural language processing tasks, including text summarization (Antiqueira et al, 2009), keyword extraction(Beliga et al, 2015(Beliga et al, , 2016, text genre detection(Grabska-Gradzińska et al, 2012;Martinčić-Ipšić et al, 2016b) and document classification…”

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confidence: 99%

The Influence of Feature Representation of Text on the Performance of Document Classification

2019

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In this paper we perform a comparative analysis of three models for feature representation of text documents in the context of document classification. In particular, we consider the most often used family of models bag-of-words, recently proposed continuous space models word2vec and doc2vec, and the model based on the representation of text documents as language networks. While the bag-of-word models have been extensively used for the document classification task, the performance of the other two models for the same task have not been well understood. This is especially true for the network-based model that have been rarely considered for representation of text documents for classification. In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated the three models and their variants. The results of the empirical comparison show that the commonly used bag-of-words model has performance comparable to the one obtained by the emerging continuous-space model of doc2vec. In particular, the low-dimensional variants of doc2vec generating up to 75 features are among the top-performing document representation models. The results finally point out that doc2vec shows a superior performance in the tasks of classifying large

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confidence: 99%