Customer sentiment appraisal from user-generated product reviews: a domain independent heuristic algorithm

Raghupathi, Dilip; Yannou, Bernard; Farel, Romain; Poirson, Émilie

doi:10.1007/s12008-015-0273-4

Cited by 14 publications

(6 citation statements)

References 27 publications

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“…To examine the relationships among customer perceived value, service quality and customer satisfaction, this study uses online customer ratings. Spontaneous online reviews and ratings of tourism and hospitality products and services posted by customers on OTAs are considered a reliable source of information (Raghupathi et al, 2015), able to provide noncommercial, detailed, rich, experiential, fast, freely accessible and up-to-date information (Gao et al, 2018a;Pahlevan Sharif and Mura, 2019;Yoo and Gretzel, 2011). While primary data obtained using traditional methods, such as interviews, questionnaires and surveys suffer from the impact of the test situation (McGue and Bouchard, 1998), OTAs provide a platform for customers to express themselves without fear, pressure, intimidation or incentives (Lewis and Van Horn, 2013), and as such, represent an authentic and trustworthy source of information about products and services (Gao et al, 2018b).…”

Section: Data Collectionmentioning

confidence: 99%

Using online travel agent platforms to determine factors influencing hotel guest satisfaction

Wong

Rasoolimanesh

Sharif

2020

JHTT

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Purpose This study aims to investigate the relationships between service quality, perceived value and hotel guest satisfaction, drawing upon data from TripAdvisor – an online travel agent (OTA) platform. The study also investigates the mediating role of perceived value on the relationship between service quality and satisfaction, as well as the moderating role of hotel star ratings on all direct and indirect relationships. Design/methodology/approach Data for this study were collected via Web scraping from August–October 2018. Data were collected from 192 three- to five star-rated hotels in Kuala Lumpur, Malaysia. Partial least squares – structural equation modeling was used for data analysis. Furthermore, importance-performance map analysis (IPMA) was performed to identify the most important items of service quality and perceived value in improving customer satisfaction. Findings The findings of this study provide support for all direct and indirect relationships for three-star and four- and five-star hotels. Moreover, the results indicate that perceived value mediates the relationship between service quality and customer satisfaction. These results support the moderating role of hotel star ratings for the relationship between service quality and perceived value. The results also show that after perceived value, three-star hotels looking to improve customer satisfaction should prioritize improving the quality of their services, sleep quality, cleanliness and rooms. Four- and five-star hotels, on the other hand, should prioritize service, cleanliness, room and sleep quality. Originality/value OTA platforms collect a wealth of data pertaining to large number of hotels; nevertheless, few studies to date have drawn on this data to examine a pre-determined conceptual framework developed based on the literature. As such, this study makes a valuable methodological contribution to the tourism and hospitality literature. In terms of theoretical contributions, this study examines the mediating role of perceived value between service quality and satisfaction using OTA data. In addition, this study assesses the moderating role of hotel star ratings for the direct and indirect effects of service quality on satisfaction. Using IPMA, this study compares the importance and performance of service quality indicators to generate satisfaction between three-star and four- and five-star hotels.

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Section: Data Collectionmentioning

confidence: 99%

Using online travel agent platforms to determine factors influencing hotel guest satisfaction

Wong

Rasoolimanesh

Sharif

2020

JHTT

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“…The technique has played role in opinion extraction and summarization (Hu & Liu, 2006). This text summarization engulfs text parsing (Archak, Ghose, & Ipeirotis, 2007Hu & Liu, 2006) with POS tagging used in the above-cited group, along with Aravindan & Ekbal (2014); Bafna & Toshniwal (2013); Ingale & Phursule (2014) ; Raghupathi, Yannou, Farel, & Poirson (2015). Classification algorithms also find a vivid place in the literature, to meet desired outcomes.…”

Section: Textual Analysis Of Online Reviewsmentioning

confidence: 90%

“…Bai (2011) proposes a heuristic search-enhanced Markov blanket model to accomplish sentiment classification, by using word dependency logic. Raghupathi et al (2015) did sentiment classification based on heuristics. The authors developed an algorithm using text parsing, followed by usage of a Dictionary of Affect Language to rate word tree leaves and finally a series of basic heuristics to calculate backward an overall sentiment rating for the review.…”

Section: Sentiment Analysismentioning

confidence: 99%

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

Anand

Srivastava

Rakshit

2017

PAJAIS

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The onset of e-marketplace, virtual communities and social networking has appreciated the influential capability of online consumer reviews (OCR) and therefore necessitate conglomeration of the body of knowledge. This article attempts to conceptually cluster academic literature in both management and technical domain. The study follows a framework which broadly clusters management research under two heads: OCR Assessment and OCR Implication (business implication). Parallel technical literature has been reviewed to reconcile methodologies adopted in the analysis of text content on the web, majorly reviews. Text mining through automated tools, algorithmic contribution (dominant majorly in technical stream literature) and manual assessment (derived from the stream of content analysis) has been studied in this review article. Literature survey of both the domains is analyzed to propose possible area for further research. Usage of text analysis methods along with statistical and data mining techniques to analyze review text and utilize the knowledge creation for solving managerial issues can possibly constitute further work.

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“…The analysis did not further discern which specific aspects of each product feature were being discussed. Phraselevel identification focused on extracting expressions or phrases from review sentences that contained the customer's opinion [14], [18], [21], [24]. This phrase-level identification allowed for the most intuitive verification of customer opinions.…”

Section: Literature Reviewmentioning

confidence: 99%

Contextual Meaning-Based Approach to Fine-Grained Online Product Review Analysis for Product Design

Park,

Joung

2024

IEEE Access

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Customers share their opinions about a product through online reviews. Companies incorporate customer opinions into product design to increase customer satisfaction and market success. Many studies have attempted to analyze customer opinions on specific product features. However, these studies do not attempt to understand the actual intent of customers when mentioning a product feature in the context of a review. To overcome this limitation, this study develops a contextual meaning-based approach that considers the contextual meanings of product features in reviews. This approach enables a deeper understanding of the intent behind the target features of a product. First, a large language model-based word embedding model and clustering algorithms are introduced to divide product features into sub-features based on contextual meanings. Second, a new method is developed for creating a contextual word map to interpret the clustering results. Third, a sentiment analysis is performed to evaluate customer satisfaction for each sub-feature using BERT. A case study of a television product was conducted to demonstrate the applicability of the developed approach. The results showed that, unlike in other studies, several sub-features were identified from four targeted TV features based on their contextual meanings. Furthermore, customer satisfaction was evaluated for each sub-feature. This study is the first attempt at providing a fine-grained online product review analysis based on unsupervised learning to clarify meanings according to the context. The developed approach can be useful for determining detailed directions for improvement in the product design process and is expected to offer a new perspective.INDEX TERMS Bidirectional encoder representations from transformers (BERT), contextual meaning, customer opinion, online product review, polysemy word, product design, product feature, sentiment analysis, sub-feature identification, television (TV).

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Customer sentiment appraisal from user-generated product reviews: a domain independent heuristic algorithm

Abstract: International audienc

Cited by 14 publications

References 27 publications

Using online travel agent platforms to determine factors influencing hotel guest satisfaction

Using online travel agent platforms to determine factors influencing hotel guest satisfaction

Assessment, Implication, and Analysis of Online Consumer Reviews: A Literature Review

Contextual Meaning-Based Approach to Fine-Grained Online Product Review Analysis for Product Design

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