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Arabic aspect based sentiment analysis using bidirectional GRU based models

arXiv 2101.10539 ↗First posted 23 Jan 2021cs.CLcited by 3

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Abstract (arXiv)

Aspect-based Sentiment analysis (ABSA) accomplishes a fine-grained analysis that defines the aspects of a given document or sentence and the sentiments conveyed regarding each aspect. This level of analysis is the most detailed version that is capable of exploring the nuanced viewpoints of the reviews. The bulk of study in ABSA focuses on English with very little work available in Arabic. Most previous work in Arabic has been based on regular methods of machine learning that mainly depends on a group of rare resources and tools for analyzing and processing Arabic content such as lexicons, but the lack of those resources presents another challenge. In order to address these challenges, Deep Learning (DL)-based methods are proposed using two models based on Gated Recurrent Units (GRU) neural networks for ABSA. The first is a DL model that takes advantage of word and character representations by combining bidirectional GRU, Convolutional Neural Network (CNN), and Conditional Random Field (CRF) making up the (BGRU-CNN-CRF) model to extract the main opinionated aspects (OTE). The second is an interactive attention network based on bidirectional GRU (IAN-BGRU) to identify sentiment polarity toward extracted aspects. We evaluated our models using the benchmarked Arabic hotel reviews dataset. The results indicate that the proposed methods are better than baseline research on both tasks having 39.7% enhancement in F1-score for opinion target extraction (T2) and 7.58% in accuracy for aspect-based sentiment polarity classification (T3). Achieving F1 score of 70.67% for T2, and accuracy of 83.98% for T3.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 25 of its 78 references that are in the map. First posted on arXiv 23 Jan 2021.

Builds on

25 of 78 references.

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13 more
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Cited by year

Citing papers per year: 2021: 1; 2022: 0; 2023: 1; 2024: 1.

3 papers.

  1. 2021Arabic aspect sentiment polarity classification using BERTcited by 1
  2. 2023weighted CapsuleNet networks for Persian multi-domain sentiment analysiscited by 1
  3. 2024Enhancing Aspect-based Sentiment Analysis in Tourism Using Large Language Models and Positional Informationcited by 1