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    <titleInfo>
        <title>Interactive Learning Approach for Arabic Target-Based Sentiment Analysis</title>
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    <name type="personal">
        <namePart type="given">Husamelddin</namePart>
        <namePart type="family">Balla</namePart>
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    <name type="personal">
        <namePart type="given">Marisa</namePart>
        <namePart type="family">Llorens Salvador</namePart>
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    <name type="personal">
        <namePart type="given">Sarah</namePart>
        <namePart type="given">Jane</namePart>
        <namePart type="family">Delany</namePart>
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        <dateIssued>2021-09</dateIssued>
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            <title>Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)</title>
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            <namePart type="given">Ruslan</namePart>
            <namePart type="family">Mitkov</namePart>
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            <namePart type="given">Galia</namePart>
            <namePart type="family">Angelova</namePart>
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    <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.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.14</url>
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        <date>2021-09</date>
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            <start>111</start>
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%0 Conference Proceedings
%T Interactive Learning Approach for Arabic Target-Based Sentiment Analysis
%A Balla, Husamelddin
%A Llorens Salvador, Marisa
%A Delany, Sarah Jane
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Held Online
%F balla-etal-2021-interactive
%X 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.
%U https://aclanthology.org/2021.ranlp-1.14
%P 111-120

Markdown (Informal)

[Interactive Learning Approach for Arabic Target-Based Sentiment Analysis](https://aclanthology.org/2021.ranlp-1.14) (Balla et al., RANLP 2021)

• Interactive Learning Approach for Arabic Target-Based Sentiment Analysis (Balla et al., RANLP 2021)

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..