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        <title>ReviewCraft : A Word2Vec Driven System Enhancing User-Written Reviews</title>
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        <namePart type="given">Pradnya</namePart>
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        <namePart type="given">Jyoti</namePart>
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            <namePart type="given">Sobha</namePart>
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    <abstract>The significance of online product reviews has become indispensable for customers in making informed buying decisions, while e-commerce platforms use them to fine tune their recommender systems. However, since review writing is purely a voluntary process without any incentives, most customers opt out from writing reviews or write poor-quality ones. This lack of engagement poses credibility issues as fake or biased reviews can mislead buyers who rely on them for informed decision-making. To address this issue, this paper introduces a system that suggests product features and appropriate sentiment words to help users write informative product reviews in a structured manner. The system is based on Word2Vec model and Chi square test. The evaluation results demonstrates that the reviews with recommendations showed a 2 fold improvement both, in the quality of the features covered and correct usage of sentiment words, as well as a 19% improvement in overall usefulness compared to reviews without recommendations. Keywords: Word2Vec, Chi-square, Sentiment words, Product Aspect/Feature.</abstract>
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%0 Conference Proceedings
%T ReviewCraft : A Word2Vec Driven System Enhancing User-Written Reviews
%A Sawant, Gaurav
%A Bhagat, Pradnya
%A D. Pawar, Jyoti
%Y D. Pawar, Jyoti
%Y Lalitha Devi, Sobha
%S Proceedings of the 20th International Conference on Natural Language Processing (ICON)
%D 2023
%8 December
%I NLP Association of India (NLPAI)
%C Goa University, Goa, India
%F gaurav-etal-2023-reviewcraft
%X The significance of online product reviews has become indispensable for customers in making informed buying decisions, while e-commerce platforms use them to fine tune their recommender systems. However, since review writing is purely a voluntary process without any incentives, most customers opt out from writing reviews or write poor-quality ones. This lack of engagement poses credibility issues as fake or biased reviews can mislead buyers who rely on them for informed decision-making. To address this issue, this paper introduces a system that suggests product features and appropriate sentiment words to help users write informative product reviews in a structured manner. The system is based on Word2Vec model and Chi square test. The evaluation results demonstrates that the reviews with recommendations showed a 2 fold improvement both, in the quality of the features covered and correct usage of sentiment words, as well as a 19% improvement in overall usefulness compared to reviews without recommendations. Keywords: Word2Vec, Chi-square, Sentiment words, Product Aspect/Feature.
%U https://aclanthology.org/2023.icon-1.60
%P 629-635

Markdown (Informal)

[ReviewCraft : A Word2Vec Driven System Enhancing User-Written Reviews](https://aclanthology.org/2023.icon-1.60) (Sawant et al., ICON 2023)

• ReviewCraft : A Word2Vec Driven System Enhancing User-Written Reviews (Sawant et al., ICON 2023)

ACL

• Gaurav Sawant, Pradnya Bhagat, and Jyoti D. Pawar. 2023. ReviewCraft : A Word2Vec Driven System Enhancing User-Written Reviews. In Proceedings of the 20th International Conference on Natural Language Processing (ICON), pages 629–635, Goa University, Goa, India. NLP Association of India (NLPAI).