Interactive Learning Approach for Arabic Target-Based Sentiment Analysis
Husamelddin Balla, Marisa Llorens Salvador, Sarah Jane Delany
Abstract
Recently, the majority of sentiment analysis researchers focus on target-based sentiment analysis because it delivers in-depth analysis with more accurate results as compared to traditional sentiment analysis. In this paper, we propose an interactive learning approach to tackle a target-based sentiment analysis task for the Arabic language. The proposed IA-LSTM model uses an interactive attention-based mechanism to force the model to focus on different parts (targets) of a sentence. We investigate the ability to use targets, right, and left context, and model them separately to learn their own representations via interactive modeling. We evaluated our model on two different datasets: Arabic hotel review and Arabic book review datasets. The results demonstrate the effectiveness of using this interactive modeling technique for the Arabic target-based task. The model obtained accuracy values of 83.10 compared to SOTA models such as AB-LSTM-PC which obtained 82.60 for the same dataset.- Anthology ID:
- 2021.ranlp-1.14
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
- Month:
- September
- Year:
- 2021
- Address:
- Held Online
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 111–120
- Language:
- URL:
- https://aclanthology.org/2021.ranlp-1.14
- DOI:
- Cite (ACL):
- Husamelddin Balla, Marisa Llorens Salvador, and Sarah Jane Delany. 2021. Interactive Learning Approach for Arabic Target-Based Sentiment Analysis. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 111–120, Held Online. INCOMA Ltd..
- Cite (Informal):
- Interactive Learning Approach for Arabic Target-Based Sentiment Analysis (Balla et al., RANLP 2021)
- PDF:
- https://preview.aclanthology.org/bionlp-24-ingestion/2021.ranlp-1.14.pdf