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This study aims to explore the role of the linguistic style used in the brand-posted social media content on consumer engagement in the Fintech domain.

A total of 3,286 tweets (registering nearly 1.35 million impressions) published by 10 leading Fintech unicorns in India were extracted using the Twitter API. The Linguistic Inquiry and Word Count (LIWC) dictionary was used to analyse the linguistic characteristics of the shared tweets. Negative Binomial Regression (NBR) was used for testing the hypotheses.

This study finds that using drive words and cognitive language increases consumer engagement with Fintech messages via the central route of information processing. Further, affective words and conversational language drive consumer engagement through the peripheral route of information processing.

The study extends the literature on brand engagement by unveiling the effect of linguistic features used to design social media messages.

The study provides guidance to social media marketers of Fintech brands regarding what content strategies best enhance consumer engagement. The linguistic style to improve online consumer engagement (OCE) is detailed.

The study’s findings contribute to the growing stream of Fintech literature by exploring the role of linguistic style on consumer engagement in social media communication. The study’s findings indicate the relevance of the dual processing mechanism of elaboration likelihood model (ELM) as an explanatory theory for evaluating consumer engagement with messages posted by Fintech brands.

This study aims to investigate the content of hotel brands’ mission statements (MSs) and their relationship with selected attributes of hotel brands.

Content analysis of hotel brands’ MSs was used to detect the MSs’ key words, which were further processed by methods of social network analysis, complemented by clustering techniques and correspondence analysis on the generalized aggregated lexical tables, a special type of correspondence analysis.

Hotel brands operating in luxurious markets more often emphasize experiences than those in midscale markets. Furthermore, hotel brands with longer traditions and those with a large number of controlled rooms communicate words in their MSs that represent a rather traditional approach to hospitality. Younger hotel brands with fewer controlled rooms chose words that indicate a more commercially oriented approach. Finally, cluster analysis revealed four dimensions of hotel brands’ MSs, instead of the nine most typically used in mission statement component models.

Understanding the frequencies and networks of keywords, and their relationship with hotel brand attributes, will help create more focussed MSs. This will strengthen hotel brands, raise their revenues and subsequently increase company performance.

The analysis provides valuable insight into MSs in the specific tourism context of hotel brands. The authors have achieved this with the use of a wide range of advanced network analytic methods. These insights can guide hotel brands to better position themselves in the competitive tourism accommodation market.

Purpose: Students’ transition from education to employment is influenced by factors like the length and calibre of their education, demography, labour market conditions, and the general state of the economy. Regardless of the economy, education systems should seek to ensure that students have the skills required for the labour market. This will help them better transition from school to work. This study examines the work skills that companies require for entry-level positions in Romania.

Need for Study: Previously, text analysis studies treated the job market only for the IT industry in Romania. To understand the demand-side opportunities and restrictions, assessing the employment opportunities for young people in the Romanian labour market is necessary.

Methodology: A text mining approach from 842 unstructured data of the existing job positions in October 2022 for fresh graduates or students is used in this chapter. The study uses data from LinkedIn job descriptions in the Romanian job market. The methodology involved is focused on text retrieval, text-pre-processing, word cloud analysis, network analysis, and topic modelling.

Findings: The empirical findings revealed that the most common words in job descriptions are experience, team, work, skills, development, knowledge, support, data, business, and software. The correlation network revealed that the most correlated pairs of words are gender–sexual–race–religion–origin–diversity–age–identity–orientation–colour–equal–marital.

Practical Implications: This study looked at the job market and used text analytics to extract a space of skill and qualification dimensions from job announcements relevant to the Romanian employment market instead of depending on subjective knowledge.

This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.

This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.

Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.

This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.

This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.

This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.

The identification of network user relationship in Fancircle contributes to quantifying the violence index of user text, mining the internal correlation of network behaviors among users, which provides necessary data support for the construction of knowledge graph.

A correlation identification method based on sentiment analysis (CRDM-SA) is put forward by extracting user semantic information, as well as introducing violent sentiment membership. To be specific, the topic of the implementation of topology mapping in the community can be obtained based on self-built field of violent sentiment dictionary (VSD) by extracting user text information. Afterward, the violence index of the user text is calculated to quantify the fuzzy sentiment representation between the user and the topic. Finally, the multi-granularity violence association rules mining of user text is realized by constructing violence fuzzy concept lattice.

It is helpful to reveal the internal relationship of online violence under complex network environment. In that case, the sentiment dependence of users can be characterized from a granular perspective.

The membership degree of violent sentiment into user relationship recognition in Fancircle community is introduced, and a text sentiment association recognition method based on VSD is proposed. By calculating the value of violent sentiment in the user text, the annotation of violent sentiment in the topic dimension of the text is achieved, and the partial order relation between fuzzy concepts of violence under the effective confidence threshold is utilized to obtain the association relation.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

This research presents and evaluates a method for assessing the entrepreneurial mindset (EM) of students in higher education.

The research considers EM a multi-variable psychological construct, which can be broken down into several conceptual sub-categories. Using data from a master course in entrepreneurship, the authors show how these categories can be applied to analyze students’ written reflections to identify linguistic markers of EM.

The research reports three main findings: analyzing student reflections is an appropriate method to explore the state and development of students’ EM; the theoretically-derived EM categories can be nuanced and extended with insight from contextualized empirical insights; and student reflections reveal counter-EM categories that represent challenges in the educator’s endeavor to foster students’ EM.

The commitment of resources to researching EM requires the dedication of efforts to develop methods for assessing the state and development of students’ EM. The framework can be applied to enhance the theoretical rigor and methodological transparency of studies of EM in entrepreneurship education.

The framework can be of value to educators who currently struggle to assess if and how their educational design fosters EM attributes.

This inquiry contributes to the critical research discussion about how to operationalize EM in entrepreneurship education studies. The operationalization of a psychological concept such as EM is highly important because a research focus cannot be maintained on something that cannot be studied in a meaningful way.

This study aims to describe and analyze the differences in the linguistic styles of honest and dishonest stories.

This paper uses a descriptive study with a multivariate analysis of linguistic categories according to the story. The research analyzed 37 honest stories and 15 dishonest stories produced during actual legal proceedings through software Linguistic Inquiry and Word Count (LIWC).

The authors find that individuals who engage in deception use a different number of words when they narrate facts. The results suggest a need for additional investigation of the linguistic style approach because of its high applicability and detection accuracy. This approach should be complemented by other types of verbal, nonverbal and psychophysiological deception detection techniques.

Among the limitations, the authors consider length of the stories should be considered and scarce scientific literature in Spanish to compare with outcomes in English.

This research highlights the relevance to include linguistic style in real contexts to differentiate honest and dishonest stories due to objectivity and agility to implement.

Understanding deception as a social behaviour and its psychological processes associated are elements that contribute to people and justice to comprehend it.

Analyzing real statements and discriminate differences in linguistic style, contribute to understand deeply this important behaviour to propose new methodologies and theories to explain it.

In this chapter, we focus on specially designed instruction (SDI) as a core value for the field of specific learning disabilities (SLD). SDI is at the heart of special education, and the field of LD has been built on the core value that effective instruction improves student outcomes. We describe a two-step test and an extended example of what is and is not SDI for Matt, a student with an SLD. Finally, we discuss some of the confusion surrounding SDI and the need for the field to return to its core value of individualized, intentional, targeted, evidence- or high leverage practice–based, and systematic instruction for students with SLD.

Using sentiment analysis (SA), this study aims to examine the impact of COVID-19 on mental health and virtual learning experiences among 1,125 students at a public Argentinean faculty.

A study was conducted during the COVID-19 pandemic, surveying 1,125 students to gather their opinions. The survey data was analysed using text mining tools and SA. SA was used to extract the students’ emotions, views and feelings computationally and identify co-occurrences and patterns in related words. The study also examines educational policies implemented after the pandemic.

The prevalent emotions expressed in the comments were trust, sadness, anticipation and fear. A combination of trust and fear resulted in submission. Negative comments often included the words “virtual”, “virtual classroom”, “virtual classes” and “professor”. Two significant issues were identified: teachers’ inexperience with virtual classes and inadequate server infrastructure, leading to frequent crashes. The most effective educational policies addressed vital issues related to the “virtual classroom”.

Text mining and SA are valuable tools for decision-making during uncertain times, such as the COVID-19 pandemic. They can also provide insights to recover quality assurance processes at universities impacted by health concerns or external shocks.

The paper makes two main contributions: it conducts a SA to gain insights from comments and analyses the relationship between emotions and sentiments to identify optimal educational policies. The study pioneers exploring the link between emotions, policies and the pandemic at a public university in Argentina. This area of research still needs to be explored.