A hybrid deep learning network for modelling opinionated content

Abstract

The ability to accurately understand opinionated content is critical 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 classification. 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 Neural 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.

Utilization of deep learning and semantic analysis for opinion mining in information extraction: a review

“…The Internet has radically transformed the way individuals convey their opinions. They can now publish product reviews on their purchasing pages and have their say in online forums, discussion groups, and blogs, known as user-created content [38]. This online term refers to the latest and quantifiable resources with various practical applications.…”