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.- Anthology ID:
- 2023.icon-1.60
- Volume:
- Proceedings of the 20th International Conference on Natural Language Processing (ICON)
- Month:
- December
- Year:
- 2023
- Address:
- Goa University, Goa, India
- Editors:
- Jyoti D. Pawar, Sobha Lalitha Devi
- Venue:
- ICON
- SIG:
- SIGLEX
- Publisher:
- NLP Association of India (NLPAI)
- Note:
- Pages:
- 629–635
- Language:
- URL:
- https://aclanthology.org/2023.icon-1.60
- DOI:
- Cite (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).
- Cite (Informal):
- ReviewCraft : A Word2Vec Driven System Enhancing User-Written Reviews (Sawant et al., ICON 2023)
- PDF:
- https://preview.aclanthology.org/fix-dup-bibkey/2023.icon-1.60.pdf