This study investigates how organisations respond to Corporate Sustainability Reporting Directive (CSRD) requirements through systematic analysis of corporate communications and sustainability reports. We employ multi-dimensional text mining analysis of 500 companies across 12 industries, using...

This systematic review investigates sentiment analysis of news headlines in the banking sector, a field susceptible to public sentiment, as demonstrated by phenomena like bank runs leading to rapid deposit withdrawals. We trace the evolution of analytic methods from traditional machine learning...

Sustainability has become a crucial factor in the financial sector, making the assessment of a company's sustainability performance essential for informed decision-making. Recognizing the media's power to shape public perception of corporate sustainability issues, this study examines the use of...

The correspondence of historical personalities serves as a rich source of psychological, social, and economic information. Letters were indeed used as means of communication within the family circles but also a primary method for exchanging information with colleagues, subordinates, and...

Most of peer-to-peer (P2P) online borrowers are small business managers. The learning behavior of borrowers in the P2P market is rarely studied. The aim of this paper is to identify the existence of borrowers' learning behavior in the P2P market using a large sample from renrendai.com, which is...

To support energy transition and climate action, China has announced its "dual carbon" goals and introduced a series of related policies. However, these policies remain disorganised and scattered across various platforms and formats, posing significant challenges for accessibility and effective...

The purpose of this study is to identify airline asymmetric attributes that affect customer satisfaction based on the three-factor theory and to build an Asymmetric Impact-Sentiment Analysis (AISA) for resource allocation prioritization. We identified food and beverage service as a basic factor;...

Global supply chains are increasingly complex and vulnerable, requiring new approaches for detecting and managing risks. Text mining, a branch of natural language processing, can extract insights from unstructured online data such as news, reports, and social media. This paper presents a...

Purpose: The purpose of our study is to explore the influence of Metaverse on FinTech industry and identify the mediators between them. Design/methodology/approach: To achieve the purpose, we conducted words frequency analysis, network analysis, and association rules analysis, based on 68,957...

Online booking of homestays through e-travel portals is based on the virtual brand and perception, which are largely affected by user-generated electronic word-of-mouth (eWOM). With the objective of mining actionable insights from eWOM, this study conducted opinion mining for homestays located...

The EU's Cohesion Policy (CP) intends to promote European economic, social, and territorial cohesion. The policy occupies about a third of the EU budget and it is the most evaluated of all EU policies. With the support of the German Ministry of Finance, two in-depth ZEW research papers by...

We study the Latent Dirichlet Allocation model, a popular Bayesian algorithm for text analysis. We show that the model's parameters are not identified, which suggests that the choice of prior matters. We characterize the range of values that the posterior mean of a given functional of the...

This paper investigates the impact of news media information on improving short-term GDP growth forecasts by analyzing a large and unique corpus of 12.4 million news articles spanning from 1991 to 2018. We extract business cycle-related sentiment from each article using an annotated dataset from...

Mit der UN-Agenda 2030 der und dem Onlinezugangsgesetz verpflichtet sich Deutschland zu einer nachhaltigen Entwicklung und der Digitalisierung von Verwaltungsleistungen. Digitalisierung und Nachhaltigkeit sind zwei Querschnittsthemen. Sie ergänzen sich und haben inhaltliche Schnittmengen. So...

Determining the sentiment of a written text is an important task in text research. This task can be performed either in the supervised or unsupervised version. In this paper, we propose a novel unsupervised algorithm for documents written in any language using documents written in Polish as an...

We develop a text-based measure of intangible investment intensity derived from firms' 10-K filings, and offer a general methodology for semantic theme scoring (STS). Our approach further classifies disclosure text into knowledge, customer, and organization capital. Firms with high intangible...

Introduction to Text Analytics in Marketing: A Practical Guide for Students and Researchers -- Textual Data, String Handling, Regular Expressions, and Data Structures -- Obtaining Textual Data for Marketing Analytics, Web Scraping, APIs, and Structured Sources -- Basic Text Analysis,...

This book is designed as a foundational textbook for upper-level undergraduate and graduate students from non-technical fields who want to acquire a basic understanding of data science and learn practical skills in data analysis. It distinguishes itself by combining theoretical knowledge with...

Purpose: The objective of the paper is to find trends of research in relic tourism-related topics. Specifically, this paper uncovers all published studies having latent issues with the keywords “relic tourism” from the Web of Science database.Methods: A total of 109 published articles...

We review several existing text analysis methodologies and explain their formal application processes using the open-source software R and relevant packages. Several text mining applications to analyze central bank texts are presented

Due to the omnipresence of digital technologies in the economy, measuring firm digitalisation is of high importance. However, current indicators show several shortcomings, e.g., they lack timeliness and regional granularity. In this study, we show that advances in text mining and comprehensive...

This study aims to investigate the material loss review published by the Federal Deposit Insurance Corporation (FDIC) on 98 failed banks from 2008 to 2015. The text mining techniques via machine learning, i.e. bag of words, document clustering, and topic modeling, are employed for the...