Daniel Dan

The paper investigates the internal migration in Italy between 1930 and 2010 in a longitudinal perspective. Risk and distances of migration of first and second order are analysed for people with age 20-49. Data are collected using an ad hoc CATI (Computer-Assisted Telephone Interviewing) system survey, involving around 2,000 respondents born and resident in Italy. A survival model is used for measuring risk of out-migration and OLS (Ordinary least squares) regression models for distance. The variables that emerge as most influencing the risk of migration are the subject’s place of residence and previous life-history. Migratory distance greatly depends on the migrants’ socio-economic background and reason for migrating. The main determinants of internal migration at individual level are education and having previously migrated at a younger age.

This chapter gives an overview of the various approaches and methods for cluster analysis and links them with the most relevant marketing research contexts. We also provide pointers to the specific packages and functions for performing cluster analysis using the R ecosystem for statistical computing. A substantial part of this chapter is devoted to the illustration of applying different clustering procedures to a reference data set of shopping basket data. We briefly outline the general approach of the considered techniques, provide a walk-through for the corresponding R code required to perform the analyses, and offer some interpretation of the results.

Advances in natural language generation (NLG) have facilitated technologies such as digital voice assistants and chatbots. In this research, we demonstrate how NLG can support content marketing by using it to draft content for the landing page of a website in search engine optimization (SEO).

This project explores the application of Natural Language Processing (NLP) techniques to analyze United Nations General Assembly (UNGA) speeches. Using NLP allows for the efficient processing and analysis of large volumes of textual data, enabling the extraction of semantic patterns, sentiment analysis, and topic modeling. Our goal is to deliver a comprehensive dataset and a tool (interface with descriptive statistics and automatically extracted topics) from which political scientists can derive insights into international relations and have the opportunity to gain a nuanced understanding of global diplomatic discourse.

This study examines the words and situations that trigger and those that do not trigger a hotel response when customers post negative online feedback. The research explores through sentiment analysis, bigrams, trigrams, and word networking the valence of online reviews of five important hotels in Las Vegas. Only the feedback that has been categorized as negative by the algorithm is selected. In correspondence to this feedback, the existence of answers from the hotels is checked together with the response style. While the negative valence of the feedback can represent a mixture of subjective and objective emotions, there are common features in their expression. On the responses side from the hotel, not all the reviews receive attention. As such, the negative feedback words are extracted and separated into those that belong to reviews that obtain a response and those that do not. The responses are standard by following an established pattern. This paper aims to contribute to a prominent issue in tourism that is little tackled: responses to feedback. The findings may help the hotel’s management to explore different paths in improving their services and their responses alike. Behavioral marketing researchers might want to use these results to confirm the existence of such patterns in different datasets or in different situations.