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    <titleInfo>
        <title>Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach</title>
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    <name type="personal">
        <namePart type="given">Alimuddin</namePart>
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        <namePart type="given">Anna</namePart>
        <namePart type="family">Jurek-Loughrey</namePart>
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    <name type="personal">
        <namePart type="given">Deepak</namePart>
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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>Online reviews are an essential aspect of online shopping for both customers and retailers. However, many reviews found on the Internet lack in quality, informativeness or helpfulness. In many cases, they lead the customers towards positive or negative opinions without providing any concrete details (e.g., very poor product, I would not recommend it). In this work, we propose a novel unsupervised method for quantifying helpfulness leveraging the availability of a corpus of reviews. In particular, our method exploits three characteristics of the reviews, viz., relevance, emotional intensity and specificity, towards quantifying helpfulness. We perform three rankings (one for each feature above), which are then combined to obtain a final helpfulness ranking. For the purpose of empirically evaluating our method, we use review of four product categories from Amazon review. The experimental evaluation demonstrates the effectiveness of our method in comparison to a recent and state-of-the-art baseline.</abstract>
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        <url>https://aclanthology.org/2021.ranlp-1.109</url>
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        <date>2021-09</date>
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            <start>959</start>
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%0 Conference Proceedings
%T Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach
%A Melleng, Alimuddin
%A Jurek-Loughrey, Anna
%A P, Deepak
%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 melleng-etal-2021-ranking
%X Online reviews are an essential aspect of online shopping for both customers and retailers. However, many reviews found on the Internet lack in quality, informativeness or helpfulness. In many cases, they lead the customers towards positive or negative opinions without providing any concrete details (e.g., very poor product, I would not recommend it). In this work, we propose a novel unsupervised method for quantifying helpfulness leveraging the availability of a corpus of reviews. In particular, our method exploits three characteristics of the reviews, viz., relevance, emotional intensity and specificity, towards quantifying helpfulness. We perform three rankings (one for each feature above), which are then combined to obtain a final helpfulness ranking. For the purpose of empirically evaluating our method, we use review of four product categories from Amazon review. The experimental evaluation demonstrates the effectiveness of our method in comparison to a recent and state-of-the-art baseline.
%U https://aclanthology.org/2021.ranlp-1.109
%P 959-967

Markdown (Informal)

[Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach](https://aclanthology.org/2021.ranlp-1.109) (Melleng et al., RANLP 2021)

• Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach (Melleng et al., RANLP 2021)

ACL

• Alimuddin Melleng, Anna Jurek-Loughrey, and Deepak P. 2021. Ranking Online Reviews Based on Their Helpfulness: An Unsupervised Approach. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 959–967, Held Online. INCOMA Ltd..