This study investigates the media representation of female entrepreneurs in newspaper and magazine articles from 2014 to 2022. By employing a quantitative content analysis of 192 articles, the study examines dominant narratives, entrepreneurial identities, ethical behaviors, challenges depicted,...

Central bank communication has become a crucial tool for steering the monetary policy stance and shaping the outlook of market participants. Traditionally, analyzing central bank communication required substantial human effort, expertise, and resources, making the process time-consuming. The...

This paper investigates the effects of air pollution on public mood using sentiment analysis of geolocated social media data. Analyzing approximately 7 million twitter posts from the United States in July 2015, we examine how fluctuations in air quality caused by Canadian wildfires influence...

This study explores consumer sentiments towards telemedicine and telehealth services using data from various digital platforms. Leveraging Brand24 for real-time data collection and NVivo 14 for in-depth content analysis, the research examines netizen reviews to reveal their perceptions,...

This study aims to address the common issue of biased estimation errors in time series modeling by analyzing the error in locating ideal hyperparameters and defining appropriate validation methods. Specifically, it focuses on predicting the stock price of Bank Rakyat Indonesia using a...

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...

This paper investigates the effects of air pollution on public mood using sentiment analysis of geolocated social media data. Analyzing approximately 7 million twitter posts from the United States in July 2015, we examine how fluctuations in air quality caused by Canadian wildfires influence...

Purpose - is to provide evidence of how social networks act as an indispensable channel in the cryptocurrency phenomenon and its public perception, analysing the context in which it occurs, as well as the patterns followed and the most commonly used channels. Research methodology - this article...

This study investigates the integration of sentiment analysis with machine learning models to forecast stock price movements using the Nvidia Corporation as a case study. Sentiment scores were derived from Nvidia-related financial news headlines using two sentiment analysis tools: FinBERT, a...

In the context of the impressive development of Big Data, AI algorithms have proven their efficiency in processing and analyzing large volumes of data. Price prediction was no exception. In the modern economic fields, the need for advanced prediction models, with increased efficiency, has become...

Central bank communication has become a crucial tool for steering the monetary policy stance and shaping the outlook of market participants. Traditionally, analyzing central bank communication required substantial human effort, expertise, and resources, making the process time-consuming. The...

User-generated content continues to shape global tourism trends, yet Africa's growing tourism sector has received limited attention. This study addresses this gap by investigating tourist sentiments and satisfaction across ten African destinations from 2018 to 2023. Employing a mixed method...

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;...

Accurate gold price forecasting is essential for informed financial decision-making, as gold is sensitive to economic, political, and social factors. This study presents a hybrid framework for multivariate gold price prediction that integrates classical econometric modelling, traditional machine...

This study investigates the media representation of female entrepreneurs in newspaper and magazine articles from 2014 to 2022. By employing a quantitative content analysis of 192 articles, the study examines dominant narratives, entrepreneurial identities, ethical behaviors, challenges depicted,...

This study examines the impact of financial disclosure timing on the sentiment of corporate reports within an emerging market context. Leveraging a comprehensive dataset covering quarterly reports from all firms listed on Borsa Istanbul between 2018 and 2023, we categorized firms into three...

This study examines the impact of the tone used in communication by two major international organizations, the United Nations (UN) and the International Monetary Fund (IMF), on electricity prices in Türkiye, an energy-dependent developing country with the highest-growing energy demand among...

This study examines the evolution of sentiment in the sustainability reports of Turkish companies from 2014 to 2023, serving as an indicator of organisational change in response to shifting sustainability regulations. A longitudinal sample of corporate sustainability reports was subjected to...

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 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 study examines the evolution of sentiment in the sustainability reports of Turkish companies from 2014 to 2023, serving as an indicator of organisational change in response to shifting sustainability regulations. A longitudinal sample of corporate sustainability reports was subjected to...

This work introduces a novel method for sentiment analysis in the stock market by combining Deep Neural Networks (DNN) and an improved Firefly Algorithm (FA). It is essential to comprehend investor sentiment in financial markets in order to forecast stock price movements and make wise investment...

This study uses natural language processing (NLP) techniques to analyze the political discourse of the surprise presidential candidate, focusing on linguistic patterns, sentiment distribution, and recurring themes. This study addresses the problem of how TikTok virality and algorithmic...

The emergence of neobanks is considered a change in the current banking model and has caused a change in consumer expectations regarding their financial experience and sustainable technology. This research analyzes users feelings, words and opinions about neobanks, and determines the critical...

Anthropomorphic AI-based chatbots are reshaping human-machine interactions, enabling users to form emotional bonds with AI agents. While these systems provide companionship and engagement, they also raise concerns regarding digital entrapment: a complex and circular causal loop, progressively...

Massive Open Online Courses (MOOCs) and the broad adoption of distance learning over the past few years have caused a remarkable shift in the educational landscape. However, the vast majority of available MOOCs often challenge learners in selecting courses that align with their academic goals,...

The dynamic field of financial markets is constantly in search of new ways to understand complex market dynamics. The increasing availability of vast amounts of text data offers new avenues for investigation (Botchway et al., 2020). This study aims to shed light on the dynamics between stock...

We study the effect of terrorist attacks on the migration discourse in the European Parliament (EP). First, using an LLM model, we develop an original dataset on sentiments of all parliamentary speeches concerning migration for 2009-2019, building on a novel dataset by Sylvester et al. (2023)....

In 1936, John Maynard Keynes proposed that emotions and instincts are pivotal in decision-making, particularly for investors. Both positive and negative moods can influence judgments and decisions, extending to economic and financial choices. Intuitions, emotional states, and biases...

It is well known that politicians speak differently when campaigning. The shadow of elections may affect candidates' change in tone during campaigns. However, to date, we lack a systematic study of the changes in communication patterns between campaign and non-campaign periods. In this study, we...

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...

The current Special Issue brought out the newest trends in Econophysics that have made use of the most recent available tools, such as Big Data. The emphasis of the reprint is on deciphering the effects of current world events, such as the repercussions of the recent war conflicts, the oil and...