Recommending products to customers using opinion mining of online product reviews and features
Abstract
E-Commerce sites are gaining popularity across the world. People visit them not just to shop products but also to know the opinion of other buyers and users of products. Online customer reviews are helping consumers to decide which products to buy and also companies to understand the buying behavior of consumers. In this paper we have created a prototype web based system for recommending and comparing products sold online. We have used natural language processing to automatically read reviews and used Naive Bayes classification to determine the polarity of reviews. We have also extracted the reviews of product features and the polarity of those features. We graphically present to the customer, the better of two products based on various criteria including the star ratings, date of review, the helpfulness score of the review and the polarity of reviews.
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The accuracy and targeting precision of an ad may contribute to higher privacy concerns.
“On the other hand, the accuracy and targeting precision of an ad may contribute to higher privacy concerns [86].”
From Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting (2019) · cites this paper as [86]
The effects of personalized ads may significantly differ according to their positioning, placement, level of personalization, and personality traits of users, such as online safety concerns.
“The results from the previous research thus imply that the effects of personalized ads may significantly differ according to their positioning, placement, level of personalization, and personality traits of the users, such as online safety concerns.”
From Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting (2019) · cites this paper as [86]
Cites
Twitter mood predicts the stock market
Bollen, J., Mao, H., & Zeng, X. (2011) · Journal of Computational Science · doi:10.1016/j.jocs.2010.12.007 · Cited by 5,206 · see citing works
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Semeradova, T., & Weinlich, P. (2019) · IEEE Access · doi:10.1109/access.2019.2948401 · Cited by 39 · see citing works
“closely to all their needs and desires [10,11,12,13].”
BackgroundJacovi, A., Caciularu, A., Goldman, O., & Goldberg, Y. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.308 · Cited by 36 · see citing works
Jacovi, A., Caciularu, A., Goldman, O., & Goldberg, Y. (2023) · arXiv · doi:10.48550/arxiv.2305.10160 · Cited by 36 · see citing works
- Authors
- Rajeev, P. V., & Rekha, V. S.
- Affiliations
- Amrita Vishwa Vidyapeetham (IN)
- ORCID
- Rajeev, P Venkata
- Venue
- 2015 International Conference on Circuits, Power and Computing Technologies [ICCPCT-2015]
- Publisher
- IEEE
- Published
- 2015-03
- Pages
- 1-5
- OpenAlex
- W1600261743
- Cited by
- 46
- References
- 1
- Citations by year
- 2024: 2 · 2023: 6 · 2022: 3 · 2021: 10 · 2020: 5 · 2019: 7 · 2018: 2 · 2017: 7 · 2016: 3 · 2015: 1
- FWCI
- 2.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Circuit, Power and Computing Technologies (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Sociology and Political Science › Digital Marketing and Social Media
- Keywords
- product recommendation, online customer reviews, sentiment analysis, e-commerce, product feature extraction, naive Bayes, star ratings, review helpfulness, product comparison, recommender systems
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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