A critical assessment of consumer reviews: A hybrid NLP-based methodology

Biswas, Baidyanath; Sengupta, Pooja; Kumar, Ajay; Delen, Dursun; Gupta, Shivam

doi:10.1016/j.dss.2022.113799

Cited by 20 publications

(4 citation statements)

References 66 publications

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“…WOM volume and valence are by far the most popular metrics in traditional WOM research (Godes and Mayzlin, 2004;Hartmann, et al, 2022). Aside from that, researchers have explored other metrics such as review variance (Yin et al, 2022), credibility (Wang et al, 2022) helpfulness (Chou et al, 2022), narrativity (Van Laer et al, 2019) and relevance (Biswas, et al, 2022).…”

Section: Online Wom Communication and Product Adoptionmentioning

confidence: 99%

Information consistency as response to pre-launch advertising communications: The case of YouTube trailers

Kampani

Nicolaides²

2023

Front. Commun.

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IntroductionPre-launch advertising communications are critical for the early adoption of experiential products. Often, companies release a variety of advertising messages for the same product, which results in a lack of information consistency. Research on the effect of advertising communications with different message content is scarce. Further, most studies on information consistency rely on experimental methods, leaving the actual effect of consumer response on product adoption unknown.MethodsTreating online comments to movie trailers as consumer response to advertising communication, we propose a natural language processing methodology to measure information consistency. We validate our measurement through an online experiment and test it on 1.3 million YouTube comments.ResultsOur empirical results provide evidence that information consistency driven by trailer-viewing is a key driver of opening box office success.DiscussionInsights deriving from this study are important to marketing communications research, especially in contexts where early product adoption is critical.

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Section: Online Wom Communication and Product Adoptionmentioning

confidence: 99%

Information consistency as response to pre-launch advertising communications: The case of YouTube trailers

Kampani

Nicolaides²

2023

Front. Commun.

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“…From the perspective of software engineering, user feedback data of applications is a rich information source for improving software quality, and there is a very close relationship between user feedback data and product quality [1]. However, there are still many challenges in mining and ltering quality-related data from massive amounts of information [7]. There is a lack of research on how to construct a complete processing work ow and conduct empirical research, leaving signi cant room for exploration [6].…”

Section: Related Researchmentioning

confidence: 99%

Practice on Framework for Product Quality Analysis Based on User Feedback Data

Chen,

Zhang,

Jiang

et al. 2023

Preprint

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Online products generate vast amounts of user feedback data, which has become crucial for companies to improve product quality and customer satisfaction. This paper proposes the FPQA-UFD (framework to analyze product quality based on user feedback data) using data mining algorithms, natural language processing, multi-classification methods, and statistical analysis, providing detailed data support for product development teams' decision-making. The framework effectively extracts information from user feedback, accurately dividing 305,311 user feedback data into 44 effective topics and extracting explanatory keywords. A multi-classification experiment achieved a classification accuracy and recall rate of 83%. This study offers valuable insights for businesses and academia to enhance decision-making and software development through user feedback analysis.

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“…Reference [15] proposed an easy-to-implement online review text analysis procedure through text mining for studying brand image and brand positioning. Reference [16] also used text exploration to build four predictors to analyze the interrelationships between reviewer credibility, review age, and review variance. Most of the existing in-app advertising studies adopted questionnaires and interviews.…”

Section: Text Miningmentioning

confidence: 99%