Deriving the Pricing Power of Product Features by Mining Consumer Reviews

By Nikolay Archak, Anindya Ghose, Panagiotis G. Ipeirotis. 2011

1,033 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Marketing, Sociology and Political Science

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Citations in the corpus

Strongly agree or strongly disagree?: Rating features in Support Vector Machines (2016, background): “2010), customer targeting (Archak et al. 2011, Cui et al. 2006), and surveillance (Fang et al. 2013). In Supervised Classi cation, we are given a set of objects Ω partitioned into classes and the aim is to build a procedure for classifying new objects. In its simplest form, each object i ∈ Ω has ass”
The impact of social and conventional media on firm equity value: A sentiment analysis approach (2013, background): “Recent research suggests that it is important to extract the multifaceted textual content in UGC, which highlights the need to delve deeper into the content of the online discussions [1,13,35].”
An Empirical Examination of the Antecedents and Consequences of Contribution Patterns in Crowd-Funded Markets (2013, neutral): “, online product reviews) in the prediction of subsequent sales performance (Archak et al. 2011).”
Mine Your Own Business: Market-Structure Surveillance Through Text Mining (2012, background): “Future research could further explore heterogeneity in market structure based on participant and message characteristics (e.g., Ghose and Ipeirotis 2011).”
Ratings lead you to the product, reviews help you clinch it? The mediating role of online review sentiments on product sales (2014, methodology): “Quantitative work has investigated how reviews influence purchase (see, for example, [1,9,6]).”
Product sales forecasting using online reviews and historical sales data: A method combining the Bass model and sentiment analysis (2017, background): “Archak, Ghose, and Ipeirotis (2011) used the programming interface providedbyAmazonWeb Services to collect daily product prices and product ratings from consumer reviews on Amazon and combined natural language processing and crowdsourcing on Amazon Mechanical Turk to extract opinions from online…”
Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook (2018, positive): “(2012); Archak et al. (2011); Ghose et al. (2012)). Our NLP methods closely mirror cutting edge multi-step methods used in the financial services industry to automatically extract financial information from textual sources (e.g., Hassan et al. (2011)) and are similar in flavor to winning algorithms ”
Sentence-Based Text Analysis for Customer Reviews (2016, background): “• Eliciting product attributes and consumers preferences for attributes (Lee and Bradlow 2011; Archak et al. 2011).”
A Study on the Impact of Linguistic Persuasive Styles on the Sales Volume of Live Streaming Products in Social E-Commerce Environment (2021, background): “emotional opinions in online reviews will form Internet word of mouth, which in turn will have a significant impact on product sales [32,33].”
What makes a great MOOC? An interdisciplinary analysis of student retention in online courses (2013, background): “Moreover, Archak et al. (2007; 2011) demonstrated how textual data can be used for both predicting future changes in sales and learning consumers' relative preferences for different product features.”
Research Note - The Impact of External Word-of-Mouth Sources on Retailer Sales of High-Involvement Products (2012, methodology): “Although the relationship between sales and sales rank has not been identified specifically for electronics products, the power law relationship has been used in related studies (Archak et al. 2011).”