A Hybrid Deep Learning Network for Modelling Opinionated Content Pantelis Agathangelou University of Nicosia Nicosia, Cyprus agathangelou.p@live.unic.ac.cy Ioannis Katakis University of Nicosia Nicosia, Cyprus katakis.i@unic.ac.cy ABSTRACT The ability to accurately understand opinionated content is crit- ical for a large set of applications. Models targeting at learning from such content should overcome the inherent difficulties of the data. We propose a novel hybrid neural network embedded in a deep learning framework that can be used for sentiment classifica- tion. Our method consists of an independent set of feed forward learning models that are able to identify rich linguistic patterns through recurrent semantic trees. We evaluate our method in four sentiment classification problems that include both binary and multi-class classification tasks. Moreover, we compare our model’s prediction accuracy with state-of-the-art methods. We observe that our method outperforms the alternative approaches. The strengths of the proposed approach are due to i) a novel Convolutional Neu- ral Network which can be employed autonomously or as part of a greater framework, ii) a hybrid framework which consists of a set of independent blocks that propagates information and improve the classification task. KEYWORDS Sentiment Analysis, Opinion Mining, Deep Learning ACM Reference Format: Pantelis Agathangelou and Ioannis Katakis. 2019. A Hybrid Deep Learning Network for Modelling Opinionated Content. In The 34th ACM/SIGAPP Symposium on Applied Computing (SAC ’19), April 8–12, 2019, Limassol, Cyprus. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3297280. 3297570 1 INTRODUCTION Online content like user opinionated reviews, contains informa- tion that if exploited appropriately may provide commercial and research value. Assigning sentiment to sentences (or phrases) is a trivial task in most cases. Trying to assess the sentiment for a large piece of text (like a product review) however, might be a little bit more challenging. This is mainly because language might include a mixed distribution of sentiment. In this paper, in order to alleviate the above limitation, we in- troduce a convolution to the widely used recurrent deep learning model. The contributions of this paper can be summarized in Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). SAC ’19, April 8–12, 2019, Limassol, Cyprus © 2019 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-5933-7/19/04. https://doi.org/10.1145/3297280.3297570 Embedding Matrix Non-Linear Layers (pǝ) PredicƟon Layer (b) Emb. Dimension (d) Filters (w ǝ) T awsome device hardlyusemy laptopanymore . (a) some lag on certain apps and overlock help . Global Sentence Emb. (x) i Bi-LSTM Classical Layer (c) e1 . . . . . . . ez e1 . . . . . . . ez ConvoluƟon Layers Sentence (w ) 1:z Sentence emb. (bǝ ) i:d (cǝ ) i:d Figure 1: Model Architecture. the following points. a) We introduce a Deep Hybrid Neural Net- work for modeling opinionated content. b) We introduce a Neural Network Hyper-Parameter that joins mixed content motifs. c) We experiment over a set of known datasets and demonstrate how our proposed approaches outperform the state-of-the-art. d) We share our deep learning model in Python/Tensorflow with the community in order to promote the research efforts in the field. 2 OUR METHOD (HYCOR) Some principles of our method (Hybrid Convolution Recurrent Neural Network) were inspired by the work of [1], [2], and [3]. Overview. HyCoR is organized into three feed forward blocks. In the first “sentence level" block (see Fig 1(a)) we use a multi-filter convolution layer to