پژوهش های برتر | پروپوزال آماده، مدیریت استراتژیک شرکت ها، پلن های کسب و کار
عنوان انگلیسی مقاله:
Modeling customer satisfaction from unstructured data using a Bayesian approach
ترجمه عنوان مقاله: استفاده از رویکرد بیزی: مدلسازی رضایت مشتری از داده های غیر ساختاری
رشته: مدیریت بازاریابی
سال انتشار: ۲۰۱۶
تعداد صفحات مقاله انگلیسی: ۳۵ صفحه
منبع: ساینس دایرکت
نوع فایل: pdf
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Abstarct
The Internet is host to many sites that collect vast amounts of opinions about products and services. These opinions are expressed in written language, and this paper presents a method for modeling the aspects of overall customer satisfaction from free-form written opinions. Written opinions constitute unstructured input data, which are first transformed into semi-structured data using an existing method for aspect-level sentiment analysis. Next, the overall customer satisfaction is modeled using a Bayesian approach based on the individual aspect rating of each review. This probabilistic method enables the discovery of the relative importance of each aspect for every unique product or service. Empirical experiments on a data set of online reviews of California State Parks, obtained from TripAdvisor, show the effectiveness of the proposed framework as applied to the aspect-level sentiment analysis and modeling of customer satisfaction. The accuracy in terms of finding the significant aspects is 88.3%. The average R2 values for predicted overall customer satisfaction using the model range from 0.892 to 0.999.
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Introduction
Organizational decision-making increasingly relies on Decision Support System (DSS) tools. During the past 30 years, research on machine learning has enabled these DSS tools to become progressively more intelligent [1]. In particular, machine learning algorithms have enabled DSS to learn and to be responsive to changing decision-making environments. In this paper, we aim to expand on how machine learning can be applied to a dynamic decision environment where we can improve our understanding of customer satisfaction based on online product or service reviews.
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