Report
A Recurrent Neural Network for Sentiment Quantification
| العنوان: | A Recurrent Neural Network for Sentiment Quantification |
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| المؤلفون: | Esuli, Andrea, Fernández, Alejandro Moreo, Sebastiani, Fabrizio |
| المصدر: | Final version published in Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM 2018), Torino, IT, 2018 |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Computation and Language, Statistics - Machine Learning, I.2.6, I.2.7 |
| الوصف: | Quantification is a supervised learning task that consists in predicting, given a set of classes C and a set D of unlabelled items, the prevalence (or relative frequency) p(c|D) of each class c in C. Quantification can in principle be solved by classifying all the unlabelled items and counting how many of them have been attributed to each class. However, this "classify and count" approach has been shown to yield suboptimal quantification accuracy; this has established quantification as a task of its own, and given rise to a number of methods specifically devised for it. We propose a recurrent neural network architecture for quantification (that we call QuaNet) that observes the classification predictions to learn higher-order "quantification embeddings", which are then refined by incorporating quantification predictions of simple classify-and-count-like methods. We test {QuaNet on sentiment quantification on text, showing that it substantially outperforms several state-of-the-art baselines. Comment: Accepted for publication at CIKM 2018 |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1145/3269206.3269287 |
| URL الوصول: | http://arxiv.org/abs/1809.00836 |
| رقم الانضمام: | edsarx.1809.00836 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1145/3269206.3269287 |
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