Detect Review Manipulation by Leveraging Reviewer Historical Stylometrics in Amazon, Yelp, Facebook and Google Reviews

Sadman, Nafiz; Gupta, Kishor Datta; Haque, Ariful; Poudyal, Subash; Sen, Sajib

doi:10.1145/3387263.3387272

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…In response to traders' deception, the proposed model generated high-quality and diverse reviews [69]. The significance of vigilance in the face of manipulation on large online platforms was highlighted by stylometry-based algorithms that detected misleading online reviews [70].…”

Section: Review Analysis and Managementmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

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In recent years, e-commerce platforms have replaced conventional marketplaces. People are rapidly adopting internet shopping due to the convenience of making purchases from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

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Section: Review Analysis and Managementmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2024

Preprint

View full text Add to dashboard Cite

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“…In response to tradersʹ deception, the proposed model generated high-quality and diverse reviews [69]. The significance of vigilance in the face of manipulation on large online platforms was highlighted by stylometry-based algorithms that detected misleading online reviews [70].…”

Section: Review Analysis and Managementmentioning

confidence: 99%

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Malik,

Bilal

2023

Preprint

View full text Add to dashboard Cite

In recent years, e-commerce platforms have replaced conventional marketplaces. People are fast adopting online shopping since they can purchase from home. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer evaluations, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges. This study covers 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the bulk of existing research relies on Amazon user reviews. Additionally, recent research has stressed the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates researchers' expanding interest and the field's continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

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“…Although the first thing that comes to mind when referring to RS is mostly related to movies and books, multiple RS implementations for products exist as well, since businesses and companies need to get closer to all the different types of customers. Apart from the automated versions of RSs that are being used in multiple enterprises and that are distributed by big companies [8] (e.g., Microsoft, Amazon, IBM), this manuscript follows a bottom-up approach towards the most computationally efficient RSs' approaches. Two (2) of these approaches, which are being further evaluated, are studied in this section, namely the (i) Surprise library and (ii) LightFM.…”

Section: -Literature Reviewsmentioning

confidence: 99%

A Comparative Study of Collaborative Filtering in Product Recommendation

Patoulia

Kiourtis²,

Mavrogiorgou³

et al. 2022

Emerg Sci J

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Product recommendation is considered a well-known technique for bringing customers and products together. With applications in music, electronic shops, or almost any platform the user daily deals with, the recommendation system’s sole scope is to help customers and attract new ones to discover new products. Through product recommendation, transaction costs can also be decreased, improving overall decision-making and quality. To perform recommendations, a recommendation system must utilize customer feedback, such as habits, interests, prior transactions as well as information used in customer profiling, and finally deliver suggestions. Hence, data is the key factor in choosing the appropriate recommendation method and drawing specific suggestions. This research investigates the data challenges of recommendation systems, specifying collaborative-based, content-based, and hybrid-based recommendations. In this context, collaborative filtering is being explored, with the Surprise library and LightFM embeddings being analysed and compared on top of foodservice transactional data. The involved algorithms’ metrics are being identified and parameterized, while hyperparameters are being tuned properly on top of this transactional data, concluding that LightFM provides more efficient recommendation results following the evaluation’s precision and recall outcomes. Nevertheless, even though the Surprise library outperforms, it should be used when constructing user-friendly models, requiring low code and low technicalities. Doi: 10.28991/ESJ-2023-07-01-01 Full Text: PDF

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Detect Review Manipulation by Leveraging Reviewer Historical Stylometrics in Amazon, Yelp, Facebook and Google Reviews

Cited by 10 publications

References 5 publications

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

Natural Language Processing for Analyzing Online Customer Reviews: A Survey, Taxonomy, and Open Research Challenges

A Comparative Study of Collaborative Filtering in Product Recommendation

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