A hybrid deep learning network for modelling opinionated content

Pantelis Agathangelou, Ioannis Katakis

    Research output: Contribution to conferencePaperpeer-review

    1 Citation (Scopus)


    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.

    Original languageEnglish
    Number of pages3
    Publication statusPublished - 1 Jan 2019
    Event34th Annual ACM Symposium on Applied Computing, SAC 2019 - Limassol, Cyprus
    Duration: 8 Apr 201912 Apr 2019


    Conference34th Annual ACM Symposium on Applied Computing, SAC 2019


    • Deep Learning
    • Opinion Mining
    • Sentiment Analysis


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