TY - GEN
T1 - Interactive Learning Approach for Arabic Target-Based Sentiment Analysis
AU - Balla, Husamelddin A.M.N.
AU - Salvador, Marisa Llorens
AU - Delany, Sarah Jane
N1 - Publisher Copyright:
© 2021 Incoma Ltd. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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 contexts, 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 sentiment analysis 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.
AB - 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 contexts, 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 sentiment analysis 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.
UR - http://www.scopus.com/inward/record.url?scp=85123585291&partnerID=8YFLogxK
U2 - 10.26615/978-954-452-072-4_014
DO - 10.26615/978-954-452-072-4_014
M3 - Conference contribution
AN - SCOPUS:85123585291
T3 - International Conference Recent Advances in Natural Language Processing, RANLP
SP - 111
EP - 120
BT - International Conference Recent Advances in Natural Language Processing, RANLP 2021
A2 - Angelova, Galia
A2 - Kunilovskaya, Maria
A2 - Mitkov, Ruslan
A2 - Nikolova-Koleva, Ivelina
PB - Incoma Ltd
T2 - International Conference on Recent Advances in Natural Language Processing: Deep Learning for Natural Language Processing Methods and Applications, RANLP 2021
Y2 - 1 September 2021 through 3 September 2021
ER -