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The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

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.

Early childhood and early elementary are key times when children develop internal and external antifat attitudes; thus, it is necessary to better understand the available children’s literature around fatness.This paper aims to examine children's picture books with fat protagonists to better understand the current landscape of children's literature. Drawing on relevant literature around fat characters and the fat studies movement, this critical content analysis considers five children’s books featuring fat protagonists.

This study uses critical content analysis to analyze texts featuring fat protagonists, including two rounds of initial reading and analysis. Using lenses of critical literacy and critical multicultural analysis, the author looks for common themes, silences and absences in the texts, images and peritext.

This paper identifies themes of characters initially internalizing antifatness, then pushing back against antifat bias toward existing with joy and without stigma. Several of these texts even draw on the history of fat activism, highlighting societal critique and a potential activist component of children’s literature with fat protagonists.

The study has a small number of books, due to the limited number of texts that fit the study parameters.

The paper concludes with examples of scaffolding for teachers and parents to have conversations with young children about antifat bias while also acknowledging notable absences, particularly boy protagonists.

These themes illustrate the power of young children to push back against antifat bias and critique oppressive social structures.

There have been very few studies looking at antifatness in children’s picture books. With more books with fat protagonists coming out in the 2020s, this study offers an understanding of the themes present, while also emphasizing the need for an intersectional approach to literature with fat protagonists.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

This study investigates the effects of vertical direction and rotation of English loan brand names in East Asian languages (Chinese and Korean) on processing fluency, perceived product quality and purchase intention.

Four experiments were conducted in China and Korea, employing a 2 (vertical direction: downward vs upward) X 3 (rotation: 0°/marquee vs 90° clockwise vs 90° counterclockwise) between-subjects factorial design.

The findings showed that when the English loan Chinese brand name was displayed downward, the marquee format was preferred, while counterclockwise rotation was favored when displayed upward. In Korean, clockwise rotation was preferred for downward presentation, while counterclockwise rotation was favored for upward presentation. The effects on purchase intention were mediated by processing fluency and perceived product quality.

This research provides practical implications for global manufacturers and retailers, offering guidance on presenting brand names in East Asian languages and optimizing product packaging designs. For Chinese consumers, the marquee format is recommended for downward-oriented brand names, while counterclockwise rotation is effective for upward orientation. For Korean consumers, clockwise rotation is favored for downward presentation and counterclockwise rotation is preferred for upward presentation. Understanding linguistic habits allows the tailoring of brand presentations, enhancing brand perception and consumer responses.

This study contributes to understanding the role of cultural and linguistic influences on consumer information processing and product perception in vertical presentations of brand names.

Online medical repositories provide a platform for users to share information and dynamically access abundant electronic health data. It is important to determine whether case report information can assist the general public in appropriately managing their diseases. Therefore, this paper aims to introduce a novel deep learning-based method that allows non-professionals to make inquiries using ordinary vocabulary, retrieving the most relevant case reports for accurate and effective health information.

The dataset of case reports was collected from both the patient-generated research network and the digital medical journal repository. To enhance the accuracy of obtaining relevant case reports, the authors propose a retrieval approach that combines BERT and BiLSTM methods. The authors identified representative health-related case reports and analyzed the retrieval performance, as well as user judgments.

This study aims to provide the necessary functionalities to deliver relevant health case reports based on input from ordinary terms. The proposed framework includes features for health management, user feedback acquisition and ranking by weights to obtain the most pertinent case reports.

This study contributes to health information systems by analyzing patients' experiences and treatments with the case report retrieval model. The results of this study can provide immense benefit to the general public who intend to find treatment decisions and experiences from relevant case reports.

This article aims to help educators provide a holistic view of the LGBTQ community by highlighting children’s books that include non-parental LGBTQ characters.

The author selected over 80 children’s books honored by the American Library Association’s Rainbow Book List. Twenty-two books were analyzed that contain examples of LGBTQ adults existing beyond the homonormative nuclear family, e.g. two same-sex parents raising children.

The author discusses various ways of living represented in these books, such as chosen families, extended families, romantic partnerships and singlehood.

With the increased number of high-quality LGBTQ-inclusive children’s books published in the past decade, this study provides the foundation for educators to select various texts that reveal diverse representations of LGBTQ individuals.

Text classification is a widely accepted and adopted technique in organizations to mine and analyze unstructured and semi-structured data. With advancement of technological computing, deep learning has become more popular among academicians and professionals to perform mining and analytical operations. In this work, the authors study the research carried out in field of text classification using deep learning techniques to identify gaps and opportunities for doing research.

The authors adopted bibliometric-based approach in conjunction with visualization techniques to uncover new insights and findings. The authors collected data of two decades from Scopus global database to perform this study. The authors discuss business applications of deep learning techniques for text classification.

The study provides overview of various publication sources in field of text classification and deep learning together. The study also presents list of prominent authors and their countries working in this field. The authors also presented list of most cited articles based on citations and country of research. Various visualization techniques such as word cloud, network diagram and thematic map were used to identify collaboration network.

The study performed in this paper helped to understand research gaps that is original contribution to body of literature. To best of the authors' knowledge, in-depth study in the field of text classification and deep learning has not been performed in detail. The study provides high value to scholars and professionals by providing them opportunities of research in this area.

This study explores the relevance of film texts in Indian social studies classrooms for students at the upper primary level. It examines how different types of visual texts can facilitate awareness, critical thinking, discussion and action.

Using multimodal discourse analysis (MDA), this paper critically evaluates five selective films from regional cinemas in India and explores their teaching implications for social studies. The study argues that films are vital multimodal resources that can challenge the prevailing narratives in social studies pedagogy. It conveys how the suggestive revised taxonomy could benefit the students. It proposes a classification system for film analysis with discussion and states how films can bring out interrelated themes and encourage deeper, critical inquiry within the curriculum. The study finds discourse around the films and qualitatively stresses dialogue exchange with sentiment analysis using MAXQDA software. Developed by VERBI software, MAXQDA is a product whose name is inspired by the German Sociologist Max Weber, while the ‘QDA' stands for Qualitative Data Analysis.

The films act as multimodal texts, navigators, metaphors, communicative circuits and catalysts. The paper concludes that films can improve and expand multimodal learning of social studies in three ways: films help learners connect emotionally with the concepts, films make the learning process more appealing and extend it beyond classroom boundaries and films offer a unique insight into the socio-cultural subtleties that are often limited in textbooks.

This research pioneers an intersectionality-driven framework for film analysis in the curriculum for Indian upper primary social studies, offering innovative pedagogical tools to enrich Indian curriculum insights and bridge existing knowledge gaps.

The process of elaboration of the symbolic universe leads to important insights into the role of symbols in understanding human reasoning. Symbols become explanatory axes of universal global realities. Myths were constructed on these explanatory paths forming a superstructure of all belief systems with paraconsistent logic for the symbolism and a symbolic syntax. Myths and symbols are to be found in all cultures. Some of the most powerful and influential ones occur in popular culture since these often have the greatest immediate social impact.

Semiotic and logical development of the symbols is in mythical systems. The dissolution of the myth and the degradation of the myth's symbols constitute a long-drawn-out process in modern Western society and wherever s influence reaches. Myth is a story that may contain symbolic elements, but compared to the symbols or images of the exceptional, myth is characterized by a “story.”

Starting from a minimal definition to define myths and propose the following definition: Myth is a traditional tale that relates memorable and exemplary actions of extraordinary personages in prestigious and distant times, and myths have various forms and functions, perhaps some more clearly defined with a signifier than others, and different approaches can be combined for a better understanding of the myths. Dispensing with such simplistic assertions, and starting from a minimal definition to define myth, myth is a traditional tale that relates memorable and exemplary actions of extraordinary personages in prestigious and distant times.

Any symbol F originates in a unit that has two aspects and functions when the unit is restored. Thus, the symbol is rather “for something” than “of something” and the symbolic objects express the objects' correspondence in one unit or hendiadys. One semantic characteristic of symbols is “recognition”. The symbol F reveals a reality by means of the homogenous association of the signifier and significance in the symbol's constitution; although reality is separate, there is a homogeneous relation between the symbolizing and symbolized in symbolization.