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With the rise of online discussion and argument mining, methods that are able to analyze arguments become increasingly important. A recent study proposed the usage of agreement between arguments to represent both stance polarity and intensity, two important aspects in analyzing arguments. However, this study primarily focused on finetuning bidirectional encoder representations from transformer (BERT) model. The purpose of this paper is to propose convolutional neural network (CNN)-BERT architecture to improve the previous method.

The used CNN-BERT architecture in this paper directly uses the generated hidden representation from BERT. This allows for better use of the pretrained BERT model and makes finetuning the pretrained BERT model optional. The authors then compared the CNN-BERT architecture with the method proposed in the previous study (BERT and Siamese-BERT).

Experiment results demonstrate that the proposed CNN-BERT is able to achieve a 71.87% accuracy in measuring agreement between arguments. Compared to the previous study that achieve an accuracy of 68.58%, the CNN-BERT architecture was able to increase the accuracy by 3.29%. The CNN-BERT architecture is also able to achieve a similar result even without further pretraining the BERT model.

The principal originality of this paper is the proposition of using CNN-BERT to better use the pretrained BERT model for measuring agreement between arguments. The proposed method is able to improve performance and also able to achieve a similar result without further training the BERT model. This allows separation of the BERT model from the CNN classifier, which significantly reduces the model size and allows the usage of the same pretrained BERT model for other problems that also did not need to finetune their BERT model.

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

Sentiment analysis and emotion processing are attracting increasing interest in many fields. Computer and information scientists are developing automated methods for sentiment analysis of online text. Most of the studies have focused on identifying sentiment polarity or orientation – whether a document, usually a product or movie review, carries a positive or negative sentiment. It is time for researchers to address more sophisticated kinds of sentiment analysis. This paper aims to evaluate a particular linguistic framework called appraisal theory for adoption in manual as well as automatic sentiment analysis of news text.

The appraisal theory is applied to the analysis of a sample of political news articles reporting on Iraq and the economic policies of George W. Bush and Mahmoud Ahmadinejad to assess its utility and to identify challenges in adopting this framework.

The framework was useful in uncovering various aspects of sentiment that should be useful for researchers, such as the appraisers and object of appraisal, bias of the appraisers and the author, type of attitude and manner of expressing the sentiment. Problems encountered include difficulty in identifying appraisal phrases and attitude categories because of the subtlety of expression in political news articles, lack of treatment of tense and timeframe, lack of a typology of emotions, and need to identify different types of behaviours (political, verbal and material actions) that reflect sentiment.

The study has identified future directions for research in automated sentiment analysis as well as sentiment analysis of online news text. It has also demonstrated how sentiment analysis of news text can be carried out.

Purpose – This chapter presents 5- to 12-year-old girls in their performances of persuasion and social control among peers in their inner city Neapolitan neighborhood of the Quartieri Spagnoli. It demonstrates how Quartieri Spagnoli girls employ rhetorical practices of appiccecarse (argumentation), specifically “shutdown” attacks, in attempts to advance one's social positioning and present themselves in control of a situation, while contemporaneously creating moral order among peers. In addition, this chapter elucidates how conflict can also strengthen relational bonds through the creation of alliances.

Methodology/approach – The analysis is based on 16 months of linguistic anthropological fieldwork. Seven focal girls and 16 of their female peers were observed and video-recorded in the home and in neighborhood streets.

Findings – Quartieri Spagnoli girls deploy a grammar of social control, including threats, directives, insults, physical attacks, wit, and intonation, to influence each other's behaviors and establish alliances and social hierarchy in their peer groups. This chapter demonstrates how those who demand control present themselves as agents who have power over other subjects and who themselves cannot be acted upon.

Social implications – Girls’ rhetorical skills serve to buy them status and situational power in their peer groups, offsetting feelings of powerlessness in an environment where they are otherwise excluded from mainstream peer groups and society.

Originality/value of chapter – This chapter offers a window onto young girls’ verbal prowess in establishing respect on inner city streets, a topic that has been almost exclusively reserved for males.

The purpose of this paper is to explore the “dark nuances” of social media by identifying moral disengagement (MD) mechanisms and hostile emotions in people discussing the hosting of immigrants and examining the relationship between MD mechanisms and hostile emotions expressed online (annoyance, irritation and contempt).

The method was based on a psycho-lexicographical approach that analysed the in vivo real words, adopting a quanti-qualitative point of view. The investigation started from the case of a Facebook post in support of immigrants after a serious shipwreck causing the death of more than 700 would-be migrants. More than 10,000 comments were codified. For the comments against the hosting of such immigrants, the authors followed a codebook aimed at identifying MD mechanisms and hostile emotions.

The main findings show an interplay between different hostile negative emotions and online MD mechanisms. The greater the intensity of hostile emotions, the more the locus of disengagement moves from the unethical individual’s behaviour – for example, offering moral justifications – to the target recipient of such behaviour – for example, blaming or dehumanising.

The study could be applicable in designing and developing algorithms and technological tools aimed at preventive interventions to promote moral awareness and emotional regulation in online settings.

The study may be a useful contribution to understanding unethical orientation by identifying areas where education can intervene in reducing harmful behavioural tendencies.

This study takes together expressed hostile emotions and MD mechanisms by means of the analysis of real words in vivo through social media discussions.

Opinion mining (OM), also known as “sentiment classification”, which aims to discover common patterns of user opinions from their textual statements automatically or semi-automatically, is not only useful for customers, but also for manufacturers. However, because of the complexity of natural language, there are still some problems, such as domain dependence of sentiment words, extraction of implicit features and others. The purpose of this paper is to propose an OM method based on topic maps to solve these problems.

Domain-specific knowledge is key to solve problems in feature-based OM. On the one hand, topic maps, as an ontology framework, are composed of topics, associations, occurrences and scopes, and can represent a class of knowledge representation schemes. On the other hand, compared with ontology, topic maps have many advantages. Thus, it is better to integrate domain-specific knowledge into OM based on topic maps. This method can make full use of the semantic relationships among feature words and sentiment words.

In feature-level OM, most of the existing research associate product features and opinions by their explicit co-occurrence, or use syntax parsing to judge the modification relationship between opinion words and product features within a review unit. They are mostly based on the structure of language units without considering domain knowledge. Only few methods based on ontology incorporate domain knowledge into feature-based OM, but they only use the “is-a” relation between concepts. Therefore, this paper proposes feature-based OM using topic maps. The experimental results revealed that this method can improve the accuracy of the OM. The findings of this study not only advance the state of OM research but also shed light on future research directions.

To demonstrate the “feature-based OM using topic maps” applications, this work implements a prototype that helps users to find their new washing machines.

This paper presents a new method of feature-based OM using topic maps, which can integrate domain-specific knowledge into feature-based OM effectively. This method can improve the accuracy of the OM greatly. The proposed method can be applied across various application domains, such as e-commerce and e-government.

In this paper the authors present a study that uses Twitter to identify critical elements of customer service in the airline industry. The goal of the study was to uncover customer opinions about services by monitoring and analyzing public Twitter commentaries. The purpose of this paper is to identify elements of customer service that provide positive experiences to customers as well as to identify service processed and features that require further improvements.

The authors employed the approach of sentiment analysis as part of the netnography study. The authors processed 67,953 publicly shared tweets to identify customer sentiments about services of four airline companies. Sentiment analysis was conducted using the lexicon approach and vector-space model for assessing the polarity of Twitter posts.

By analyzing Twitter posts for their sentiment polarity the authors were able to identify areas of customer service that caused customer satisfaction, dissatisfaction as well as delight. Positive sentiments were linked mostly to online and mobile check-in services, favorable prices, and flight experiences. Negative sentiments revealed problems with usability of companies’ web sites, flight delays and lost luggage. Evidence of delightful experiences was recorded among services provided in airport lounges.

Paper demonstrates how sentiment analysis of Twitter feeds can be used in research on customer service experiences, as an alternative to Kano and SERVQUAL models.

Though paradoxes constitute a basic ontological condition of organizational processes, the modernist approach has always been to find ways to resolve them. In this chapter, an alternative approach called hermeneutic appreciation is proposed. By accepting and affirming paradoxes through the process of hermeneutic appreciation, the generative potential inherent in them can be recognized and unfolded. This chapter presents a case study of an organization that demonstrated such a sophisticated understanding of hermeneutic appreciation. By not dismissing the paradoxes but by affirming and reframing them, members of this organization were able to reverse a visible organizational decline and instead infuse it with new energy and vitality leading to an eventual organizational renewal. Lessons learned from this appreciative inquiry project suggest that a hermeneutic appreciation of paradoxes may act as a change intervention and is likely to enable innovation and organizational transformation.

In view of the changes which have taken place in this field in recent years, it is perhaps a suitable time to review the fundamentals of the subject, in order that the significance of recent developments might not be lost for the surface coatings analyst.

MBIT is a manualised mentalization-based approach to working with hard to reach young people at risk of a wide range of life adversities including severe mental illness, substance misuse, family breakdown, school exclusion, offending and homelessness. The on-line manual (www.tiddlymanuals.com) describes how Adolescent Mentalization-Based Integrative Therapy (AMBIT) is a systemic intervention requiring attention to four different domains of intervention simultaneously; much emphasis is placed on the support systems for workers to maintain this balance in what are often chaotic working conditions. The purpose of this paper is to illustrate how these four main components of the AMBIT approach link together in actual clinical practice.

The authors illustrate the core techniques of the AMBIT approach, namely, “working with your client”, “working with your team”, “ working with your network” and “learning as a team” with a series of case vignettes, demonstrating the inter-relationship of these components rather than seeing them as separate strands.

A range of mentalization-based techniques such as “thinking together”, mentalized formulation, “disintegration grids” and web-based manualising are described and illustrated in relation to a series of case vignettes in order to address barriers to effective practice. The vignettes emphasise how these components must be linked together and held in balance, and how easily they become disconnected in working with young people’s ambivalent or even hostile relationships to help.

First, developing a shared, mentalized formulation of a young person’s difficulties is an important aspect of working with highly troubled young people. Second, mentalizing is a relational process and is easily disrupted, for both workers and young people, by raised anxiety and affect, a common feature of working with this client group. AMBIT provides specific methods, for example, “thinking together” for supporting the mentalizing of individual workers in their team in an explicit way. Third, workers from different agencies may often find it difficult to make sense of each other’s behaviour and decision making. AMBIT proposes the use of a mentalizing approach to this difficulty using a technique called a disintegration grid. Finally, AMBIT proposes a new practitioner focused approach to manualising as a method by which a team can become more explicit about its methods of working in order to support systematic practice and evaluate outcomes.

The innovative AMBIT approach proposes that clinicians need to attend to team and network relationships at least as much as their relationship with the client, in addition to adopting a stance of learning as a team from their casework. A high level of clinical skill is needed to support a team to achieve this balanced approach to casework. This work is of interest to all multi-disciplinary teams working with hard to reach young people.