Comparing automated text classification methods

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.

TL;DR: A unified conceptual framework and a multi-faceted research agenda are presented that argue that machine learning methods can process large-scale and unstructured data, and have flexible model structures that yield strong predictive performance and that such methods may lack model transparency and interpretability.

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.

TL;DR: This article proposed an empirical framework and quantify the accuracy-interpretability trade-off for different types of research questions, data characteristics, and analytical resources to enable informed method decisions contingent on the application context.

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.

TL;DR: High generalization ability of support-vector networks utilizing polynomial input transformations is demonstrated and the performance of the support- vector network is compared to various classical learning algorithms that all took part in a benchmark study of Optical Character Recognition.

TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.

TL;DR: This paper proposed a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI).