A Hybrid Persian Sentiment Analysis Framework: Integrating Dependency Grammar Based Rules and Deep Neural Networks

TL;DR

This paper introduces a hybrid Persian sentiment analysis framework that combines linguistic dependency grammar rules with deep neural networks, significantly improving accuracy over existing methods on benchmark datasets.

Contribution

The novel hybrid framework integrates symbolic dependency grammar rules with deep neural networks for concept-level sentiment analysis in Persian, enhancing polarity detection accuracy.

Findings

Outperforms state-of-the-art approaches by 10-15%

Effective in concept-level sentiment analysis for Persian

Combines linguistic rules with deep learning for improved results

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.