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In this age of extremely well-developed social media, it is necessary to detect any change in the corporate image of an enterprise immediately so as to take quick action to avoid the wide spread of a negative image. However, existing survey-based corporate image evaluation methods are costly, slow and static, and the results may quickly become outdated. User comments, news reports and we-media articles on the internet offer varied channels for enterprises to obtain public evaluations and feedback. The purpose of this study is to effectively use online information to timely and accurately measure enterprises’ corporate images.

A new corporate image evaluation method was built by first using a literature review to establish a corporate image evaluation index system. Next, an automatic text analysis of online public information was performed through a topic classification and sentiment analysis algorithm based on the dictionary. The accuracy of the topic classification and sentiment analysis algorithm is then calculated. Finally, three internet enterprises were chosen as cases, and their corporate image was evaluated.

The results show that the author’s corporate image evaluation method is effective.

First, in this study, a new corporate image evaluation index system is constructed. Second, a new corporate image evaluation method based on text mining is proposed that can support data-driven decision-making for managers with real-time corporate image evaluation results. Finally, this study improves the understanding of corporate image by generating business intelligence through online information. The findings provide researchers with specific and detailed suggestions that focus on the corporate image management of emerging internet enterprises.

The article explores to what extent party politics has influenced the different trajectories in Spain and Italy in terms of gendered active social policies (ASPs) (i.e. ALMPs and WLBPs). Second, it investigates how social and political modernization in the two countries has facilitated or hindered party competition on gendered ASPs.

To investigate to what extent parties support gendered ASPs, the article relies on an original content analysis of party manifestos issued during the 2010s national elections. A total of 1387 quasi-sentences have been coded. The results were then quantified to graphically show how positions differentiate across parties and countries.

The content analysis of party manifestos displays that party politics matters: gendered ASPs are backed in a very different way by the Spanish and Italian parties. While in Spain all political parties have strongly championed ALMPs and WLBPs, this is not the case for the Italian parties. The research has also stressed that the specific path of social and political modernization is an important intervening variable that alters positively or negatively parties' support for gendered ASPs.

The article contributes to widen theoretically and empirically the literature on ASPs in the Southern European countries. Theoretically, it questions the supposed homogeneity of the Southern social model and investigated the alleged bifurcation between Italy and Spain, focusing on those policies – ASPs – that constitute the foundations of the Southern model: familialism and dualization. Furthermore, this bifurcation was analyzed adopting a gender perspective, and exploring adherence to or departure from the Southern model. Third, the article focuses on the politics of ASPs demonstrating that inspecting the political arena can contribute to explain policy change.

This chapter argues that the Americanisation of online policing has questionable impacts in Australian prosecutions involving drugs obtained and distributed through dark web cryptomarkets. The authors describe several Australian prosecutions of mid- and low-level dealers who have accessed drugs through the dark web and contrast these with the United States (US) case against the cryptomarket, AlphaBay. The discussion in this study emphasises how Australian police and courts view the relative weight of dark web activity associated with the domestic and transnational supply of illicit drugs that result in formal prosecutions. The authors suggest that large-scale forms of online and dark web police surveillance undertaken by US enforcement agencies reflect Ethan Nadelmann’s (Cops across borders: the internationalization of US criminal law enforcement, University Park: Pennsylvania State University Press, 1993) thesis on the Americanisation of global policing through transnational communications networks. The authors then explain how key elements of transnational dark web drug supply appear to have a marginal bearing on criminal investigations into low- and mid-level traffickers in Australia, which rely on conventional surveillance tactics to identify clandestine mail pickups, physical distribution methods, and irregular money trails. However, the authors then illustrate how the Americanisation of online policing that targets high-level entrepreneurs and seeks to dismantle or eliminate dark web cryptomarkets has important implications on Australian reforms aimed at enhancing online surveillance powers to target a range of crimes that are often wrongly associated with illicit drug cryptomarkets. The authors conclude by demonstrating how intensive dark web surveillance has limited direct impact on routine drug policing in Australia, with dark web communications simply another medium for facilitating the physical detection of illicit transnational drug transactions.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

This research aims to elucidate why consumers decide to eat meals that seem to be higher in calories and salt, despite their goal being to consume fewer calories and sodium. Korean participants are to be used for this study. The present research further investigated the impacts of categorization and averaging bias in relation to the health halo phenomenon, specifically focusing on traditional food and textured vegetable protein (soy meat) burgers. Thus, the present research investigated how consumers' intentions contrasted with their consumption goals in food choice circumstances.

We partitioned the survey due to the COVID-19 epidemic. A single, well trained surveyor first surveyed customers at cafés in Seoul and six other Korean cities. We received 102 in-person survey replies. A total of 254 advanced degree or undergraduate students from two universities completed an online questionnaire. There are 356 responses. Two studies were conducted where participants were instructed to evaluate the perceived healthiness, calorie content, and sodium level of different food items. The specifics of each study are elucidated in the main body of the paper.

This study shows that Koreans categorize meals as virtue or vice depending on their perceived healthiness, validating the categorization effect. Furthermore, this research demonstrated that consumers' perceptions of the health benefits of traditional meals and soy meat burgers impact their categorization. Koreans also assessed the average of the vice and virtue and found vice-virtue combination meals healthier than the vice alone. This affects how calories and sodium are perceived. This study also shown that high virtue affects averaging bias more than weak virtue in meals with vice and virtue combo.

This study extended food categorization and averaging bias to non-US consumers and confirmed this contradictory meal choice is universal. Health halo also affects food health perception. The results of this study revealed that Koreans consider traditional food healthier than western junk food. Korean customers incorrectly assume soy meat burgers have fewer calories and sodium than regular burgers. Thus, this study explains Korean consumers' food health misconceptions related to paradoxical consumption.

This study aims to determine the most similar set of recommendation books to the user selections in LibraryThing.

For this purpose, 30,000 tags related to History on the LibraryThing have been selected. Their tags and the tags of the related recommended books were extracted from three different recommendations sections on LibraryThing. Then, four similarity criteria of Jaccard coefficient, Cosine similarity, Dice coefficient and Pearson correlation coefficient were used to calculate the similarity between the tags. To determine the most similar recommended section, the best similarity criterion had to be determined first. So, a researcher-made questionnaire was provided to History experts.

The results showed that the Jaccard coefficient, with a frequency of 32.81, is the best similarity criterion from the point of view of History experts. Besides, the degree of similarity in LibraryThing recommendations section according to this criterion is equal to 0.256, in the section of books with similar library subjects and classifications is 0.163 and in the Member recommendations section is 0.152. Based on the findings of this study, the LibraryThing recommendations section has succeeded in introducing the most similar books to the selected book compared to the other two sections.

To the best of the authors’ knowledge, itis for the first time, three sections of LibraryThing recommendations are compared by four different similarity criteria to show which sections would be more beneficial for the user browsing. The results showed that machine recommendations work better than humans.

The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review.

This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products.

This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area?

The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.

The interpretation of any emerging form or period in art history was never a trivial task. However, in the case of digital art, technology, becoming an integral part, multiplied the complexity of describing, systematizing and evaluating it. This article investigates the most common metadata standards for the documentation of art as a broad category and suggests possible next steps toward an extended metadata standard for digital art.

Describing several techno-cultural phenomena formed in the last decade, manifesting the extendibility of digital art (its ability to be easily extended across multiple modalities), the article, at first, points to the long overdue need to re-evaluate the standards around it. Then it suggests a deeper analysis through a comparative study. In the scope of the study three artworks, The Arnolfini Portrait (Jan van Eyck), an iconic example of the early Renaissance, The World's First Collaborative Sentence (Douglas Davis), a classic example of early Internet art and Fake It Till You Make It (Maya Man), a prominent example of the blockchain art, are examined following the structure of the VRA Core 4.0 standard.

The comparative study demonstrates that digital art is more multi-semantic than traditional physical art, and requires new taxonomies as well as approaches for data acquisition.

Acknowledging that digital art simply has not yet evolved to the stage of being systematically collected by cultural institutions for documentation, curation and preservation, but otherwise, in the past few years, it has been at the front-center of social, economic and technological trends, the article suggests looking for hints on the future-proof extended metadata standard in some of those trends.

The purpose of this paper is to describe a new approach to sentence representation learning leading to text classification using Bidirectional Encoder Representations from Transformers (BERT) embeddings. This work proposes a novel BERT-convolutional neural network (CNN)-based model for sentence representation learning and text classification. The proposed model can be used by industries that work in the area of classification of similarity scores between the texts and sentiments and opinion analysis.

The approach developed is based on the use of the BERT model to provide distinct features from its transformer encoder layers to the CNNs to achieve multi-layer feature fusion. To achieve multi-layer feature fusion, the distinct feature vectors of the last three layers of the BERT are passed to three separate CNN layers to generate a rich feature representation that can be used for extracting the keywords in the sentences. For sentence representation learning and text classification, the proposed model is trained and tested on the Stanford Sentiment Treebank-2 (SST-2) data set for sentiment analysis and the Quora Question Pair (QQP) data set for sentence classification. To obtain benchmark results, a selective training approach has been applied with the proposed model.

On the SST-2 data set, the proposed model achieved an accuracy of 92.90%, whereas, on the QQP data set, it achieved an accuracy of 91.51%. For other evaluation metrics such as precision, recall and F1 Score, the results obtained are overwhelming. The results with the proposed model are 1.17%–1.2% better as compared to the original BERT model on the SST-2 and QQP data sets.

The novelty of the proposed model lies in the multi-layer feature fusion between the last three layers of the BERT model with CNN layers and the selective training approach based on gated pruning to achieve benchmark results.

The purpose of this paper is to analyse how an influential supplier of electronics manufacturing services (i.e. Foxconn) discloses its labour practices.

The analysis is conducted through the theoretical lens of legitimacy and impression management. This particular firm is selected as it provides a rich case on labour practice disclosures in a setting where significant labour malpractice incidents occurred from 2009 to 2011. The sample period covers 12 years of the firm's labour practice disclosures (2008–2019) to match with publicly available information that is used to construct expert comparative accounts on the disclosures. The authors corroborate the comparative accounts with sociological studies and responsibility reports from the major customer (i.e. Apple).

The authors found that the disclosures become more detailed over successive years. Occupational health and safety issues are predominantly reported, followed by issues relating to vocational guidance and training and then employment policy. Regarding impression management strategies, defensive strategies embedded in the disclosures are rarely detected and assertive strategies are persistently used from 2008 to 2019 to maintain legitimacy. The comparative accounts show the persistent use of one defensive strategy (i.e. omission) to maintain and regain legitimacy. In other words, as an economic strategy, material labour practice issues are persistently omitted in the disclosures. The incidents discernibly affect how Foxconn discloses labour practices.

The authors’ study contributes to the limited extant research on suppliers' labour practice disclosures from the perspective of legitimacy theory and impression management. The results will be of great interest to researchers, investors, assurers and other stakeholders.