Dr Kia Dashtipour K.Dashtipour@napier.ac.uk
Lecturer
A hybrid Persian sentiment analysis framework: Integrating dependency grammar based rules and deep neural networks
Dashtipour, Kia; Gogate, Mandar; Li, Jingpeng; Jiang, Fengling; Kong, Bin; Hussain, Amir
Authors
Dr. Mandar Gogate M.Gogate@napier.ac.uk
Senior Research Fellow
Jingpeng Li
Fengling Jiang
Bin Kong
Prof Amir Hussain A.Hussain@napier.ac.uk
Professor
Abstract
Social media hold valuable, vast and unstructured information on public opinion that can be utilized to improve products and services. The automatic analysis of such data, however, requires a deep understanding of natural language. Current sentiment analysis approaches are mainly based on word co-occurrence frequencies, which are inadequate in most practical cases. In this work, we propose a novel hybrid framework for concept-level sentiment analysis in Persian language, that integrates linguistic rules and deep learning to optimize polarity detection. When a pattern is triggered, the framework allows sentiments to flow from words to concepts based on symbolic dependency relations. When no pattern is triggered, the framework switches to its subsymbolic counterpart and leverages deep neural networks (DNN) to perform the classification. The proposed framework outperforms state-of-the-art approaches (including support vector machine, and logistic regression) and DNN classifiers (long short-term memory, and Convolutional Neural Networks) with a margin of 10–15% and 3–4% respectively, using benchmark Persian product and hotel reviews corpora.
Journal Article Type | Article |
---|---|
Acceptance Date | Oct 5, 2019 |
Online Publication Date | Oct 17, 2019 |
Publication Date | 2020-03 |
Deposit Date | Apr 28, 2021 |
Journal | Neurocomputing |
Print ISSN | 0925-2312 |
Publisher | Elsevier |
Peer Reviewed | Peer Reviewed |
Volume | 380 |
Pages | 1-10 |
DOI | https://doi.org/10.1016/j.neucom.2019.10.009 |
Keywords | Persian sentiment analysis, Low-Resource natural language processing, Dependency-based rules, Deep learning |
Public URL | http://researchrepository.napier.ac.uk/Output/2765743 |
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