Discovering Hidden Concepts in Predictive Models for Texts' Polarization

Abstract

The growth of Internet and the information technology has generated big changes in subjects' communication, which, nowadays, occurs through social media or via thematic forums. This challenges the traditional notion of Customer Relationship Management (CRM) and pushes businesses to prompt and accurate understanding of sentiments expressed, in order to address their marketing actions. In this paper, the authors propose a combined application of a supervised Sentiment Analysis (SA) with a probabilistic kernel discriminant to provide a robust classifier of texts polarization. The partition obtained is also described by means of a statistical characterization of the texts. Such an approach is very promising, not only in terms of classification accuracy, but also in terms of knowledge extraction. A real case study is illustrated in order to test and show the effectiveness of the proposed strategy.

Identifying Oncological Patient Information Needs to Improve e‐Health Communication: a preliminary text‐mining analysis

“…[11][12][13] In particular, in order to elicit consumer needs, statistical tools were proposed, aiming to extract keywords from textual data and to employ them to automatically classify original documents in main categories. 11,25 The extraction of keywords requires performing an association analysis on the different concepts present in the documents, but every attempt of textual analysis is particularly problematic because of the difficulty in managing the wide number of associations produced. 12 Binary Correspondence Analysis (CA) is a statistical tool commonly used to overcome this problem.…”