Unfolding the characteristics of incentivized online reviews

Costa, António Aguiar; Guerreiro, João; Moro, Sérgio; Henriques, Roberto

doi:10.1016/j.jretconser.2018.12.006

Cited by 48 publications

(33 citation statements)

References 51 publications

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“…Costa et al experimented a data mining technique to predict incentivized reviews based on some selected features such as the length of reviews, how helpful they are etc. [2]. [14] also used a text-based approach for fake news detection but considered the test, response and clustering of user features determined by support vector decomposition and integrated into a hybrid model.…”

Section: Related Workmentioning

confidence: 99%

Incentivized Comment Detection with Sentiment Analysis on Online Hotel Reviews

Imtiaz

Ahmed

Paul

2020

Preprint

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With the enormous platforms available in present days, consumers communicate and interconnect online with web users all around the world to share their experiences. Thus, online platform has become a major source of reviews about different entities. People presently travel frequently around the world for different purposes. Seeking good hotels for accommodation is a prime concern. Customer reviews on hotels help future customers to take decisions about their accommodation as well as help hotel owners to rethink about designing customer facilities. However, many online reviews are biased due to different factors. Many hotel owners come up with attractions like referral rewards, coupons, bonus points etc. to the reviewers to motivate them in writing biased reviews. We have worked on US's 100 hotel and found 952 incentivized reviews out of 19175 reviews, which is 4.96% of total reviews. A categorization on incentivized reviews is performed as well. Furthermore, hotels are distinguished based on real and incentivized reviews found on them. Results are verified using machine learning algorithms. Random Forest, K-Nearest Neighbor and Support Vector Machine are applied as machine learning algorithms to validate the accuracy of our model and their prediction results are compared. Random Forest outperforms with 94.4% prediction accuracy.

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Section: Related Workmentioning

confidence: 99%

Incentivized Comment Detection with Sentiment Analysis on Online Hotel Reviews

Imtiaz

Ahmed

Paul

2020

Preprint

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“…Five studies belonging to this cluster (Agnihotri and Bhattacharya, 2016;Eslami and Ghasemaghaei, 2018;Heng et al, 2018;Rathan et al, 2018;Costa et al, 2019) employed Amazon reviews mainly for conducting sentiment analysis. Uniquely Heng et al (2018) applied topic modelling approach by employing linear discriminant analysis (LDA) for determining helpfulness of reviews on consumers' coffee purchase decisions, and identified four factors (Amazon service, physical feature, flavor feature, and subjective expression) that impact the helpfulness of customer reviews.…”

Section: Proposed Classification Technique Increases Accuracy Of Review Polarity Calculation From %70s To %80smentioning

confidence: 99%

“…Sentiment analysis was conducted by LIWC (Linguistic Inquiry and Word Count) package, and it was found that being moderated by the reviewer experience, review content and implied sentiment are key predictors of review helpfulness. Costa et al (2019) employed over 100.000…”

Section: Proposed Classification Technique Increases Accuracy Of Review Polarity Calculation From %70s To %80smentioning

confidence: 99%

Sosyal Medya Analitiğinde Makine Öğrenmesi Uygulamaları: Literatür İncelemesi

Dobrucalı¹,

Ilter²

2020

Journal of Yaşar University

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Social media analytics (SMA), referring to the collection and analysis of user generated data from social media platforms, attract attention of both researchers and practitioners striving to derive consumer insights. The SMA domain grows multifariously, with a highlight on the capability of machine learning algorithms in capturing noteworthy insights through processing high-volume and complex data in a cost effective way. As machine learning applications draw attention as a fertile area that may re-shape the future of SMA, there is a need to comprehend trends and approaches in an integrative framework. Accordingly, this study aims to present an integrative framework by portraying machine learning application trends and approaches in SMA. 42 scientific articles published in refereed scientific business, management, and computational science journals between the years 2013 and 2019 are analyzed via systematic literature review based on visual text mining method (SLR-VTM). The results revealed five distinctive research clusters as: (1) review sites, (2) microblogs, (3) social networking sites, (4) content communities, (5) cross-media. This analysis plays a crucial role for enhancing our understanding regarding the intellectual structure of the field, acknowledging the leading studies of the domain, better positioning future research, and determining gaps and new paths for researchers.

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“…Due to the huge amount of text data from various sources, text mining techniques have been applied in several fields such as mining online reviews to discover the behavioral intentions of park visitors [16], mining online reviews for customer satisfaction analysis [5], online review classification [7], user-generated content analysis [31], service blueprint development [25] and supply chain risk management [6]. Moro et al [20] used text mining software, Tools for Innovation Monitoring, to recognize the evolving technologies of renewable energy.…”

Section: Introductionmentioning

confidence: 99%

Exploring technology opportunities and evolution of IoT-related logistics services with text mining

Chen

2021

Complex Intell. Syst.

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Many IoT technologies have been applied in the logistics industry in recent years, and they have had a substantial impact on many sectors such as shipping, air freight, warehousing, inventory, etc. Exploring technology opportunities and carrying out technological trend analysis are essential for IoT’s evolution, and there are many techniques or methods for doing so. In this paper, data analysis and text mining techniques, technology opportunity analysis (TOA) and technology-service evolution analysis (TSEA) have been applied to analyze and observe IoT technologies’ and services’ evolution. Academic journals, market reports, and patents have been collected and reviewed on the topic of IoT in the logistics field in this paper. Moreover, by using TOA, technology opportunities have been analyzed to explore IoT-related logistics services. The results of TOA, for example, show that cloud technology is essential to develop smart logistics services, and communication RFID technologies are key to developing information logistics services. Finally, TSEA enables the observation of IoT technology and logistics service evolution by combining unstructured and semi-structured data from text documents. Observing the results of TSEA, the evolution of IoT in logistics is identified, and the results of TSEA also confirm those of TOA using unstructured or semi-structured text data from documents only. The results of this paper are discussed and compared with those of some previous review studies. In summary, the results of this paper provide methodological guidelines on this topic for a comprehensive understanding of IoT-related logistics services.

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Unfolding the characteristics of incentivized online reviews

Cited by 48 publications

References 51 publications

Incentivized Comment Detection with Sentiment Analysis on Online Hotel Reviews

Incentivized Comment Detection with Sentiment Analysis on Online Hotel Reviews

Sosyal Medya Analitiğinde Makine Öğrenmesi Uygulamaları: Literatür İncelemesi

Exploring technology opportunities and evolution of IoT-related logistics services with text mining

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