Author
Renée Bunnell
Bio: Renée Bunnell is an academic researcher. The author has contributed to research in topics: Topic model & Consumer choice. The author has an hindex of 1, co-authored 1 publications receiving 36 citations.
Papers
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TL;DR: In this article, a quantitative approach for describing entertainment products, in a way that allows for improving the predictive performance of consumer choice models for these products, has been proposed to improve the prediction performance of these models.
Abstract: The authors propose a quantitative approach for describing entertainment products, in a way that allows for improving the predictive performance of consumer choice models for these products. Their ...
79 citations
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TL;DR: The authors found that words are part of almost every marketplace interaction, including online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data.
Abstract: Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But...
321 citations
04 May 2020
TL;DR: This research presents a meta-modelling framework that automates the very labor-intensive and therefore time-heavy and expensive process of manually cataloging and cataloging individual words in a language.
Abstract: Natural language processing (NLP) is gaining momentum in management research for its ability to automatically analyze and comprehend human language. Yet, despite its extensive application in manage...
111 citations
TL;DR: In this paper, the authors conduct a critical analysis of how artificial intelligence (AI) affects the essential nature of customer relationship management (CRM) and survey the AI capabilities that will transform CRM into AI-CRM and examine how the transformation will influence customer acquisition, development and retention.
103 citations
TL;DR: In this paper, the authors propose that firm strategies should be informed through the lens of the marketing ecosystem that considers the interrelated and dynamic megatrends in the modern business environment.
47 citations
TL;DR: This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR) with a thorough review of superior methods from 2000 to 2019 in CSLR researches, and methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets were compared.
Abstract: Chinese Sign Language (CSL) offers the main means of communication for the hearing impaired in China. Sign Language Recognition (SLR) can shorten the distance between the hearing-impaired and healthy people and help them integrate into the society. Therefore, SLR has become the focus of sign language application research. Over the years, the continuous development of new technologies provides a source and motivation for SLR. This paper aims to cover the most recent approaches in Chinese Sign Language Recognition (CSLR). With a thorough review of superior methods from 2000 to 2019 in CSLR researches, various techniques and algorithms such as scale-invariant feature transform, histogram of oriented gradients, wavelet entropy, Hu moment invariant, Fourier descriptor, gray-level co-occurrence matrix, dynamic time warping, principal component analysis, autoencoder, hidden Markov model (HMM), support vector machine (SVM), random forest, skin color modeling method, k-NN, artificial neural network, convolutional neural network (CNN), and transfer learning are discussed in detail, which are based on several major stages, that is, data acquisition, preprocessing, feature extraction, and classification. CSLR was summarized from some aspect as follows: methods of classification and feature extraction, accuracy/performance evaluation, and sample size/datasets. The advantages and limitations of different CSLR approaches were compared. It was found that data acquisition is mainly through Kinect and camera, and the feature extraction focuses on hand’s shape and spatiotemporal factors, but ignoring facial expressions. HMM and SVM are used most in the classification. CNN is becoming more and more popular, and a deep neural network-based recognition approach will be the future trend. However, due to the complexity of the contemporary Chinese language, CSLR generally has a lower accuracy than other SLR. It is necessary to establish an appropriate dataset to conduct comparable experiments. The issue of decreasing accuracy as the dataset increases needs to resolve. Overall, our study is hoped to give a comprehensive presentation for those people who are interested in CSLR and SLR and to further contribute to the future research.
30 citations