A Framework of Review Analysis for Enhancement of Business Decision Making

Qazi, Atika; Raj, Ram Gopal; Tahir, Muhammad; Naqvi, Syed Aon Ali

doi:10.1109/icdmw.2013.160

Cited by 3 publications

(2 citation statements)

References 15 publications

(15 reference statements)

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“…Moreover, other sources such as Foursquare, TripAdvisor, Priceline, Orbitz, hotels.com, Epinions, MovieLens, and Expedia also use a source with only one paper, and so on are cited as spam detection sources which have (16%) of the total of studies. depicted in Figure (5). We observed that 37% of the papers adopt a support vector machine (SVM), 15% are Naïve Bayes (NB), and 9% are based on the k-nearest neighbors algorithm (k-NN).…”

Section: E Major Data Sources Of Spam Detection Studiesmentioning

confidence: 99%

Machine Learning-Based Opinion Spam Detection: A Systematic Literature Review

Qazi,

Hasan,

Mao

et al. 2024

IEEE Access

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The substantial upsurge in Web 2.0 deployment allows a large number of consumers to share their opinions with potential consumers and producers through product and service review platforms. Customer decision-making relies on an important aspect of review: spam-free opinions. Unfortunately, fraudulent activities produced spam reviews that misled potential buyers and traders. Subsequently, it does stop opinion-mining techniques from reaching accurate conclusions. This study aims to identify and review existing state-of-the-art methodologies for three groups: (1) spam reviews, (2) individual spammers, and (3) group spam. The machine learning (ML) and deep learning (DL) techniques for spam detection are categorized, and we establish an assessment that may be deemed appropriate in the field. The findings reveal a total of 10 metrics, with accuracy being the most often used (25%) concerning ML-based techniques in spam detection. Followed by recall in 24% of studies, and precision in 22% of studies. In addition, the Fmeasure, the area under the curve (AUC), and F1-score evaluation metrics reveal that the use of the Amazon dataset as a whole increased by 7%. This study concludes that the majority of SMS spam filtering strategies are leading solutions. In addition, the taxonomy of existing state-of-the-art methodologies is developed, and it is concluded that a substantial number of studies utilize these existing SMS anti-spam applications. This research uncovered previously unexplored areas of ML and DL's application to spam review and provided a new paradigm for applying these technologies to the issue. The findings provide both academics and practitioners with a deeper understanding of the barriers to spam review identification, as well as potential improvement opportunities using ML techniques. These individuals will now have the chance to consider a benchmark for future research.

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Section: E Major Data Sources Of Spam Detection Studiesmentioning

confidence: 99%

Machine Learning-Based Opinion Spam Detection: A Systematic Literature Review

Qazi,

Hasan,

Mao

et al. 2024

IEEE Access

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“…All these types play a significant role in business decision making. In particular, an important aspect of every business is the effective analysis of information related to their product or service type, in order to prevail over competitors (Qazi et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

A systematic literature review on opinion types and sentiment analysis techniques

Qazi

Raj

Hardaker³

et al. 2017

Internet Research

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Purpose -The purpose of this paper is to map the evidence provided on the review types, and explain the challenges faced by classification techniques in sentiment analysis (SA). The aim is to understand how traditional classification technique issues can be addressed through the adoption of improved methods. Design/methodology/approach -A systematic review of literature was used to search published articles between 2002 and 2014 and identified 24 papers that discuss regular, comparative, and suggestive reviews and the related SA techniques. The authors formulated and applied specific inclusion and exclusion criteria in two distinct rounds to determine the most relevant studies for the research goal. Findings -The review identified nine practices of review types, eight standard machine learning classification techniques and seven practices of concept learning Sentic computing techniques. This paper offers insights on promising concept-based approaches to SA, which leverage commonsense knowledge and linguistics for tasks such as polarity detection. The practical implications are also explained in this review.Research limitations/implications -The findings provide information for researchers and traders to consider in relation to a variety of techniques for SA such as Sentic computing and multiple opinion types such as suggestive opinions. Originality/value -Previous literature review studies in the field of SA have used simple literature review to find the tasks and challenges in the field. In this study, a systematic literature review is conducted to find the more specific answers to the proposed research questions. This type of study has not been conducted in the field previously and so provides a novel contribution. Systematic reviews help to reduce implicit researcher bias. Through adoption of broad search strategies, predefined search strings and uniform inclusion and exclusion criteria, systematic reviews effectively force researchers to search for studies beyond their own subject areas and networks.

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Gender differences in information and communication technology use & skills: a systematic review and meta-analysis

et al. 2021

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Even though information and communication technology (ICT) is essential for everyday life and has gained considerable attention in education and other sectors, it also carries individual differences in its use and relevant skills. This systematic review aims to examine the gender differences in ICT use and skills for learning through technology. A comprehensive search of eight journal databases and a specific selection criterion was carried out to exclude articles that match our stated exclusion criteria. We included 42 peer-reviewed empirical publications and conference proceedings published between 2006 and 2020. For a subsample of studies, we performed a small-scale meta-analysis to quantify possible gender differences in ICT use and skills. A random-effects model uncovered a small and positive, yet not significant, effect size in favor of boys ( g = 0.17, 95% CI [−0.01, 0.36]). However, this finding needs to be further backed by large-scale meta-analyses, including more study samples and a broader set of ICT use and skills measures. We highlight several concerns that should be addressed and more thoroughly in collaboration with one another to better IT skills and inspire new policies to increase the quality of ICT use. The findings from this review further suggest implications and present existing research challenges and point to future research directions.

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A Framework of Review Analysis for Enhancement of Business Decision Making

Cited by 3 publications

References 15 publications

Machine Learning-Based Opinion Spam Detection: A Systematic Literature Review

Machine Learning-Based Opinion Spam Detection: A Systematic Literature Review

A systematic literature review on opinion types and sentiment analysis techniques

Gender differences in information and communication technology use & skills: a systematic review and meta-analysis

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