A Survey of Quality Prediction of Product Reviews

Almagrabi, Hana; Malibari, Areej; McNaught, John

doi:10.14569/ijacsa.2015.061107

Cited by 10 publications

(5 citation statements)

References 78 publications

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“…Labbe et al (Labbé et al, 2015) conducted a study using computer generated literature with the aim of detecting fake articles. Almagrabi et al (Almagrabi & Malibari, 2015) carried out a survey related to product reviews and qualitative prediction of them. In order to identify sentiments in social media activities, Kumar and Sharma (Kumar & Sharma, 2017) analysed social media data.…”

Section: Spam Detection On Social Mediamentioning

confidence: 99%

Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

2019

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Astroturfing is a phenomenon in which sponsors of fake messages or reviews are masked because their intentions are not genuine. Astroturfing reviews are intentionally made to influence people to take decisions in favour of or against a target service or product or organization. The tourism sector being one of the sectors that is flourishing and witnessing unprecedented growth is affected by the activities of astroturfers. Astroturfing reviews can cause many problems to tourists who make decisions based on available online reviews. However, authentic and genuine reviews help people make informed decisions. In this paper a Latent Dirichlet Allocation (LDA) based Group Topic-Author model is proposed for efficient discovery of social astroturfing groups within the tourism domain. An algorithm named Astroturfing Group Topic Detection (AGTD) is defined for the implementation of the proposed model. The experimental results of this study revealed the utility of the proposed system for the discovery of social astroturfing groups within the tourism domain.

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Section: Spam Detection On Social Mediamentioning

confidence: 99%

Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

2019

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“…Liu (2012), Pang and Lee (2008) and Vinodhini and Chandrasekaran (2012). Information about assessing the quality of product reviews can be found in Almagrabi, Malibari, and McNaught (2015).…”

Section: Related Workmentioning

confidence: 99%

Corpus Analysis and Annotation for Helpful Sentences in Product Reviews

Almagrabi

Malibari

McNaught

2018

CIS

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For the last two decades, various studies on determining the quality of online product reviews have been concerned with the classification of complete documents into helpful or unhelpful classes using supervised learning methods. As in any supervised machine-learning task, a manually annotated corpus is required to train a model. Corpora annotated for helpful product reviews are an important resource for the understanding of what makes online product reviews helpful and of how to rank them according to their quality. However, most corpora for helpfulness are annotated on the document level: the full review. Little attention has been paid to carrying out a deeper analysis of helpful comments in reviews. In this article, a new annotation scheme is proposed to identify helpful sentences from each product review in the dataset. The annotation scheme, guidelines and the inter-annotator agreement scores are presented and discussed. A high level of inter-annotator agreement is obtained, indicating that the annotated corpus is suitable to support subsequent research.

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“…Most works related to product clustering usually focus on analysis of customer behaviors [7] to cluster recommended products of interest [1] or analysis of product reviews [8]- [9] to study human opinions about the products features. These works usually use sentiment analyses or opinion mining [9]- [16] where human subjects are involved for assessment of the product features.…”

Section: Related Workmentioning

confidence: 99%

“…AP(f, C) denotes the average position of the feature f in vectors of cluster C, and is formulated in (8). This is used for the second criterion at (9).…”

Section: Vs ={ VI | I =1n } Represents Feature Vector Space Wherementioning

confidence: 99%

Efficient Feature Selection for Product Labeling over Unstructured Data

Yetgin¹,

Elewi²,

Gözükara³

2017

ijacsa

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Abstract-The paper introduces a novel feature selection algorithm for labeling identical products collected from online web resources. Product labeling is important for clustering similar or same products. Products blindly crawled over the web sources, such as online sellers, have unstructured data due to having features expressed in different representations and formats. Such data result in feature vectors whose representation is unknown and non-uniform in length. Thus, product labeling, as a challenging problem, needs efficient selection of features that best describe the products. In this paper, an efficient feature selection algorithm is proposed for product labeling problem. Hierarchical clustering is used with the state of the art similarity metrics to assess the performance of the proposed algorithm. The results show that the proposed algorithm increases the performance of product labeling significantly. Furthermore, the method can be applied to any clustering algorithm that works on unstructured data.

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A Survey of Quality Prediction of Product Reviews

Cited by 10 publications

References 78 publications

Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

Group topic-author model for efficient discovery of latent social astroturfing groups in tourism domain

Corpus Analysis and Annotation for Helpful Sentences in Product Reviews

Efficient Feature Selection for Product Labeling over Unstructured Data

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