Information Exchange Platform Research Articles

Abstract B032: Advancing the science of cancer in Latinos conference

Abstract Cancer is the leading cause of mortality within the Latino population. Projections suggest a 142% increase in cancer incidence among Latinos over the next two decades. This trend is concerning given that Latinos, the largest minority group in the United States, are expected to constitute at least 28% of the nation's populace by 2060. To address the need for enhanced comprehension of these trends and to identify equitable interventions targeting Latino health disparities, the Institute of Health Promotion Research and the Mays Cancer Center jointly established the biennial Advancing the Science of Cancer in Latinos (ASCL) Conference. The fourth ASCL Conference convened in San Antonio, TX from February 21-23, 2024. The conference objectives were twofold: 1) Examine the intersection of health equity and biological underpinnings in cancer control, prevention, and treatment; and 2) Foster collaborative initiatives among attendees aimed at addressing cancer-related health disparities within the Latino community. The 2024 conference attracted 286 participants compared to 251 attendees in 2022, encompassing guest speakers, researchers, physicians, healthcare professionals, patient advocates, researchers, and students. It comprised 23 sessions (3 keynotes, 6 plenaries, 6 breakouts, 3 general sessions, 1 special session, 1 lunch panel session) featuring insights from 51 speakers, alongside a poster session highlighting 80 research abstracts and a rapid-fire session tailored for early-career investigators. Sessions encompassed multiple topics relevant to Latino cancer research, spanning health disparities, clinical trials, survivorship, patient advocacy, climate change impacts on cancer, precision medicine, health policy and social determinants of health. A post-conference survey from a cohort of 48 respondents underscored high levels of satisfaction, with 90% expressing pronounced contentment regarding the conference experience, compared to 85% in 2022. Moreover, nearly all respondents (98%) rated the relevance of conference content to Latino cancer science as excellent, with 93% regarding the conference as an exceptional platform for information exchange, compared to 79% in 2022. Impressions regarding session quality were overwhelmingly positive, with 90% of respondents indicating a high level of satisfaction, and all survey respondents acknowledging the objectivity, balance, and absence of commercial bias in the session content. Our results showed that the resounding positivity in attendee feedback reflects the conference's success in fostering meaningful engagement among researchers committed to alleviating Latino cancer health disparities. The ASCL conference facilitated an understanding of cultural nuances and barriers specific to the Latino community, stimulating collaborative efforts aimed at enhancing Latino health outcomes. Overall, this retrospective analysis affirms the conference's success in achieving its stipulated objectives and underscores the need of having consistently coordinated research endeavors to reduce Latino health disparities. Citation Format: Amelie G. Ramirez, Rebecca T. Jones, Matthew P. Banegas, Maria C Camargo, Gerardo Colon-Otero, Cliff Despres, Laura Fejerman, Filipa Lynce, Martin Mendoza, Patricia I. Moreno, Alejandro Recio-Boiles, Barbara Segarra-Vazquez, Sandi Stanford, Mariana Stern, Katherine Y. Tossas, Edward J. Trapido, Nicolette M Orozco. Advancing the science of cancer in Latinos conference [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B032.

Research on the Challenges and Paths of Industry Education Integration in Vocational Colleges

This article aims to explore the challenges and effective paths that vocational colleges face in deepening the integration of industry and education. The integration of industry and education, as an important trend in the development of higher vocational education, aims to improve the quality of talent cultivation and meet market demand through the close integration of education and industry. However, vocational colleges currently face many challenges in the practice of integrating industry and education, including low enthusiasm for enterprise participation, insufficient practical experience of teachers, and poor information exchange. To address these issues, this article proposes a series of strategies and paths: firstly, to strengthen the guiding role of enterprises and enhance their enthusiasm and initiative in participating in the integration of industry and education; Secondly, strengthen the construction of the teaching staff, enhance their practical abilities and market awareness; Once again, establish an information exchange platform to promote effective communication and resource sharing between schools and enterprises; Finally, optimize the practical teaching process to enhance students' practical abilities and professional competitiveness. By implementing these strategies, this article aims to provide theoretical basis and practical guidance for vocational colleges to deepen the integration of industry and education, promote the comprehensive improvement of vocational education quality, and provide strong talent support for social and economic development.

LSTM-Based Forum Topic Classification Model

With the rapid development of information technology, forums have become an important platform for information exchange. However, the manual classification of forum topics consumes a significant amount of human resources and is prone to classification errors. To address this issue, this study proposes a forum topic classification model based on Long Short-Term Memory (LSTM) networks. By leveraging LSTM’s capability in text processing, the accuracy and efficiency of topic classification are significantly improved. This study used approximately 68,000 entries from 29 topic categories, scraped from Zhihu, for the experiments. Preprocessing steps such as text cleaning, tokenization, and word vectorization were performed, and a classification model with LSTM and Dropout layers was designed. The experimental results indicate that the model performs well in most topic classifications, although overfitting remains an issue for certain categories. The paper concludes by summarizing the advantages and limitations of the model and discusses the potential for improving classification accuracy through increasing data volume and optimizing the model in the future.

Construction Cost Optimization of Prefabricated Buildings Based on BIM Technology

Prefabricated assemblies are popular in the construction industry due to their minimal carbon footprint, enhanced safety, and reliability. A combination of software, including Revit, Navisworks, and Practical Structural Design and Construction (PKPM) software, is used to reduce costs by refining the specifications and dimensions of components, streamlining the variety of molds, enhancing design performance through rigorous component collision inspections and structural optimization, and ensuring cost-effectiveness. The integration of building integration modeling (BIM) visualization significantly diminishes errors attributable to information asymmetry and minimized material wastage stemming from production inaccuracies. The implementation of a Radio Frequency Identification (RFID) information exchange platform enables real-time component tracking, provides insights into the transportation dynamics of these components, and facilitates cost optimization during the transportation phase. Moreover, simulations conducted using Fuzor software preemptively identify potential construction site issues. There is a substantial cost savings of 710,000 yuan.

Introduction to the database “efficacy of different treatment modalities for lower respiratory tract infections in children”

AbstractThis study aimed to establish a registry database of different treatment modalities for lower respiratory tract infections (LRTIs) in Chinese children, thereby filling gaps in knowledge on clinical characteristics, treatment modalities, and rational drug use in relation to LRTIs in Chinese children, and providing large amounts of continuous, complete, scientific, and objective clinical data, and an information exchange platform. Multicenter data from these children's clinical visits were collected, pooled, and analyzed using medical informatics and statistical techniques to explore their potential value. The database was preliminarily established and a real‐world study cohort was constructed based on a total of 4805 patients registered in this database. Pneumonia was identified as the most common type of LRTIs (72.44%), followed by acute bronchitis (20.71%). The mean age of the enrolled children with LRTIs was 3.26 ± 2.84 years, and boys accounted for 59.21% of the samples. Among the enrolled children, pneumonia and acute bronchitis had the highest incidence in children aged 1–3 years (27.44%) and those aged 3–6 years (34.16%), respectively. In this national, multicenter, observational database of LRTIs in children, the real‐world characteristics and treatment modalities for LRTIs in Chinese children are elucidated. This database will help improve the research efficiency of clinicians and facilitate the exploration of underlying clinical patterns in real‐world medical big data.

Analyzing and Detecting Information Types of Developer Live Chat Threads

Online chatrooms serve as vital platforms for information exchange among software developers. With multiple developers engaged in rapid communication and diverse conversation topics, the resulting chat messages often manifest complexity and lack structure. To enhance the efficiency of extracting information from chat threads , automatic mining techniques are introduced for thread classification. However, previous approaches still grapple with unsatisfactory classification accuracy due to two primary challenges that they struggle to adequately capture long-distance dependencies within chat threads and address the issue of category imbalance in labeled datasets. To surmount these challenges, we present a topic classification approach for chat information types named EAEChat. Specifically, EAEChat comprises three core components: the text feature encoding component captures contextual text features using a multi-head self-attention mechanism-based text feature encoder, and a siamese network is employed to mitigate overfitting caused by limited data; the data augmentation component expands a small number of categories in the training dataset using a technique tailored to developer chat messages, effectively tackling the challenge of imbalanced category distribution; the non-text feature encoding component employs a feature fusion model to integrate deep text features with manually extracted non-text features. Evaluation across three real-world projects demonstrates that EAEChat, respectively, achieves an average precision, recall, and F1-score of 0.653, 0.651, and 0.644, and it marks a significant 7.60% improvement over the state-of-the-art approaches. These findings confirm the effectiveness of our method in proficiently classifying developer chat messages in online chatrooms.

Climate Change Policies and Global Cooperation: Assessing the Role of International Organizations in Germany

Purpose: The aim of the study was to analyze the climate change policies and global cooperation: assessing the role of international organizations in Germany. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: International organizations play a vital role in facilitating global cooperation on climate change policies. They serve as platforms for information exchange and coordination of initiatives, setting international standards and targets. Additionally, these organizations provide financial support and technical assistance to enhance climate resilience, particularly in developing countries. Overall, their efforts are essential for driving collective action and combating climate change on a global scale. Unique Contribution to Theory, Practice and Policy: Complex interdependence theory, transnational advocacy networks (TANs) & regime theory may be used to anchor future studies on climate change policies and global cooperation: assessing the role of international organizations. International organizations provide technical expertise, capacity-building support, and financial assistance to enhance the implementation of climate change policies at the national and regional levels. International organizations play a vital role in shaping the global policy agenda on climate change through advocacy, norm promotion, and policy coordination.

Insights into Cybercrime Detection and Response: A Review of Time Factor

Amidst an unprecedented period of technological progress, incorporating digital platforms into diverse domains of existence has become indispensable, fundamentally altering the operational processes of governments, businesses, and individuals. Nevertheless, the swift process of digitization has concurrently led to the emergence of cybercrime, which takes advantage of weaknesses in interconnected systems. The growing dependence of society on digital communication, commerce, and information sharing has led to the exploitation of these platforms by malicious actors for hacking, identity theft, ransomware, and phishing attacks. With the growing dependence of organizations, businesses, and individuals on digital platforms for information exchange, commerce, and communication, malicious actors have identified the susceptibilities present in these systems and have begun to exploit them. This study examines 28 research papers focusing on intrusion detection systems (IDS), and phishing detection in particular, and how quickly responses and detections in cybersecurity may be made. We investigate various approaches and quantitative measurements to comprehend the link between reaction time and detection time and emphasize the necessity of minimizing both for improved cybersecurity. The research focuses on reducing detection and reaction times, especially for phishing attempts, to improve cybersecurity. In smart grids and automobile control networks, faster attack detection is important, and machine learning can help. It also stresses the necessity to improve protocols to address increasing cyber risks while maintaining scalability, interoperability, and resilience. Although machine-learning-based techniques have the potential for detection precision and reaction speed, obstacles still need to be addressed to attain real-time capabilities and adjust to constantly changing threats. To create effective defensive mechanisms against cyberattacks, future research topics include investigating innovative methodologies, integrating real-time threat intelligence, and encouraging collaboration.

Electronic medical information systems and timeliness of care in the emergency department: a scoping review

BackgroundAlthough many emergency department (ED) settings have implemented an electronic medical information system (EMIS) and EMIS tools in an effort to improve timeliness of care outcomes, there remains a paucity of scientific information on this topic. We therefore performed a scoping review to describe the range of EMIS interventions and their impacts on time-based outcomes in the ED.MethodsWe searched four bibliographic databases to identify potentially relevant records published after 2014 in English. Two reviewers assessed records for eligibility using a two-step screening process. We then extracted data on the type of EMIS, outcomes assessed, and reported results. Findings were summarized in tabular form and grouped by time-based outcome.ResultsTwenty-five studies met the eligibility criteria, with approximately half being retrospective studies. Interventions varied among studies; they generally included a new or updated EMIS, EMIS tools related to disease diagnosis and/or management, triage tools, or health information exchange platforms. Included studies compared interventions with relevant comparator groups, such as prior versions of an EMIS, absence of an EMIS, pen-and-paper documentation, and/or communication via telephone and fax. The most common outcomes reported were length of stay (n = 17 studies) and time to medication (n = 6 studies), followed by time to order, time to provider, and time from result to disposition. Reported effects of the interventions were generally inconsistent, showing either improvements, delays, or no change in examined outcomes.ConclusionsAdditional research is needed to determine how electronic medical information may be used in the ED to improve timeliness of care. Findings from this review can be used to inform future systematic reviews that evaluate the impact of these systems and tools on specific quality of care measures.

An evolutionary analysis of supply chain collaborative information sharing based on prospect theory.

In order to delve into the dynamic evolution process and influencing factors of information sharing decisions among stakeholders under supply chain collaboration, this study constructs an evolutionary game model with suppliers and retailers as the primary entities. Within this model, a combined approach of game theory and prospect theory is employed, integrating prospect value functions and weight functions to create an information sharing prospect value matrix. A comprehensive analysis is conducted on the strategic choices and benefits of entities considering the psychological perception of information sharing, and critical factors influencing the stability of information sharing evolution results are explored through numerical simulations using Matlab. The key findings of this study are as follows: Firstly, from the perspective of supply chain collaboration, the probability of entities evolving into information sharing is negatively correlated with the cost of information sharing and positively correlated with the benefits generated by information coordination. Secondly, looking at supply chain collaboration, entities are more likely to engage in information sharing behavior when they exhibit a lower level of risk aversion, indicating greater rationality, when facing profits; conversely, they are more likely to participate in information sharing when they display a higher degree of risk preference, indicating less rationality, in the face of losses. Furthermore, the lesser sensitivity of suppliers and retailers to losses is more likely to drive the system towards an information-sharing state. Based on the primary findings mentioned above, this study offers recommendations for enhancing trust, constructing information exchange platforms, and adjusting psychological awareness. These suggestions contribute to improving information sharing among entities within the supply chain, thus enhancing the overall efficiency and collaboration of the supply chain.

MANIFESTATION OF COLLECTIVE SECURITY IN ASEAN AS A REGIONAL ORGANIZATION DURING PANDEMIC COVID-19

This research is entitled Manifestation of Collective Security in ASEAN as a Regional Organisation During the COVID-19 Pandemic. This research aims to identify ASEAN policies and efforts to realise collective security during the COVID-19 pandemic period. This research examines the manifestation of collective security in the Association of Southeast Asian Nations (ASEAN) as a regional organisation during the COVID-19 pandemic. The research method is to use a qualitative approach, combining desk research and data analysis. The results highlight ASEAN's important role in enhancing regional security through cooperation, particularly in the economic and diplomatic sectors, ASEAN utilises digital platforms for information exchange and coordination. The ASEAN Senior Officials Meeting on Health Development and the ASEAN Health Ministers Meeting facilitate open communication, allowing countries to share best practices and resources. For example, Thailand received early notification of COVID-19 through the ASEAN Emergency Operations Centre Network, enabling swift preparedness actions. The COVID-19 pandemic has demonstrated ASEAN's capacity to uphold its mission of regional stability and security. Through coordinated efforts, resource sharing, and international partnerships, ASEAN has effectively addressed the challenges posed by the pandemic. This experience underscores the importance of collective security and regional cooperation, reaffirming ASEAN's role as an important actor in maintaining stability and fostering resilience in Southeast Asia. This study concludes that ASEAN as a regional organisation plays an important role in ensuring the collective security of its members

Memahami Dampak Media Sosial terhadap Komunikasi Interpersonal: Pendekatan Teori Komunikasi

The use of social reality construction theory as an addition to social exchange theory is a significant step towards understanding more deeply the impact of social media on interpersonal communication. Social reality construction theory offers a powerful perspective on how social media can shape individuals' perceptions of the reality around them. In this research, the literature method is used with a focus on reviewing and analyzing literature related to the impact of social media on interpersonal communication. The research aims to conduct a critical review of interpersonal communication theory and analyze how this theory has implications for social relationships in the digital era. This research shows that overall, changes in patterns of social relations that arise as a result of social media can be understood through the lens of social exchange theory. Social media provides a platform for information exchange, support, and social interaction, however, the impact is the emergence of dynamics that can sometimes sacrifice the depth and quality of traditional interpersonal relationships.

Entanglement Routing Design Over Quantum Networks

Quantum networks have emerged as a future platform for quantum information exchange and applications, with promising capabilities far beyond traditional communication networks. Remote quantum entanglement is an essential component of a quantum network. How to efficiently design a multi-routing entanglement protocol is a fundamental yet challenging problem. In this paper, we study a quantum entanglement routing problem to simultaneously maximize the number of quantum-user pairs and their expected throughput. Our approach is to formulate the problem as two sequential integer programming problems. We propose efficient entanglement routing algorithms for these two optimization problems and analyze their time complexity and performance bounds. Evaluation results highlight that our approach outperforms existing solutions in both the number of quantum-user pairs served and network throughput.

Tiktok Sebagai Media Interaktif Pembelajaran Editing Video

The social media platform TikTok has grown rapidly as one of the main platforms for creative expression and information exchange. This article discusses the use of video editing tools provided by TikTok in creating interesting learning materials for students. We analyze TikTok's creative features including visual effects, music, and quick edits, and illustrate how to apply them to learning. This research involves a qualitative approach through case studies, focusing on the teacher's experience in creating learning materials with the TikTok video editing tool. The findings show that this approach can motivate students by providing visual content that matches the preferences of the younger generation. Students' positive response to this approach indicates increased engagement and interest in learning. However, challenges in utilizing TikTok's video editing tools are also acknowledged. Integrating creativity with substantial content and ensuring the integrity of education is maintained are some of the aspects that must be considered. Ethical and privacy implications are also discussed in the context of the use of social media platforms in education. In conclusion, TikTok's video editing tools offer exciting potential for creating impactful learning materials. With a thoughtful approach, educators can design content that fits current visual trends and communication styles, while maintaining the required educational quality.

Examining the Significance of Intercultural Communicative Competence Development in Trainees during Pre-service Training.

The aim of this study is to discuss the need for improved intercultural communicative competence (ICC) training for pre-service Chinese as a Second Language (CSL) teachers who travel abroad to teach. Previous studies have shown that pre-service CSL teachers often struggle with intercultural communication, and this study investigates the reasons behind their difficulties and suggests ways to address them. The researchers used the Intercultural Sensitivity Scale (ISS) and the Intercultural Effectiveness Scale (IES) to measure the ICC of 217 pre-service CSL teachers. The study found that while pre-service CSL teachers have strong intercultural communicative emotional ability, their behavioral ability in intercultural communication is weak. In particular, they lacked skills in interactive control, interactive relaxation, identity management, information skills, and behavioral flexibility. The study proposes several strategies to improve ICC training for pre-service CSL teachers. One potential approach is the use of Computer Assisted Language Learning to provide teacher-trainee students with opportunities to practice intercultural communication skills in simulated contexts, receive personalized feedback, and engage with authentic materials and cultural information relevant to their host country. CALL could also facilitate communication between pre-service CSL teachers and former CSL teachers, providing a platform for information exchange and additional psychological support. The study also suggests that pre-service training should distinguish between foreign language learning and intercultural communication skills training. Specifically, it recommends that foreign language proficiency should be improved, while training should focus on educational culture and work culture in the host country, rather than providing a general introduction to the national conditions of the country. Finally, the study proposes that pre-service training should establish contact between pre-service CSL teachers and former CSL teachers as early as possible so that trainees can learn specific relevant information in advance, which can help them get ready, avoid intercultural communication mistakes, and obtain additional psychological support. By incorporating CALL and addressing the areas of weakness identified in the study, pre-service CSL teachers can be better prepared to navigate the challenges of intercultural communication and provide effective and culturally sensitive instruction abroad.

Pervasive User Data Collection from Cyberspace: Privacy Concerns and Countermeasures

The increasing use of technologies, particularly computing and communication paradigms, has significantly influenced our daily lives. Interconnecting devices and networks provides convenient platforms for information exchange and facilitates pervasive user data collection. This new environment presents serious privacy challenges. User activities can be continuously monitored in both digital and physical realms. Gathered data can be aggregated and analysed, revealing aspects of user behaviour that may not be apparent from a single data point. The very items that facilitate connectivity simultaneously increase the risk of privacy breaches. The data gathered to provide services can also be used for monitoring and surveillance. This paper discerns three novel categories of privacy concerns relating to pervasive user data collection: privacy and user activity in cyberspace, privacy in personal cyber–physical systems, and privacy in proactive user-driven data collection. We emphasise the primary challenges, ranging from identity tracking in browsing histories to intricate issues in opportunistic networks, situating each within practical, real-world scenarios. Furthermore, we assess the effectiveness of current countermeasures, investigating their strengths and limitations. This paper explores the challenges in preserving privacy in user interactions with dynamic interconnected systems and suggests countermeasures to mitigate identified privacy risks.

Exploring social media adoption for marketing purpose among healthcare professionals in Gondar town, central Gondar zone: A facility-based cross-sectional survey

Background Social media has become an integral platform for global information exchange and business interactions, emerging as a crucial tool for promoting products and services in the digital age. Despite its worldwide significance, local businesses, especially in Ethiopia, lag behind in leveraging social media for healthcare marketing. The scarcity of studies on social media adoption among healthcare providers in Ethiopia highlights the imperative for comprehensive research. Objective This study, conducted in Gondar Town, focused on private healthcare professionals, aiming to identify the determinants of their behavioral intention and usage behavior in adopting social media marketing. Method A facility-based cross-sectional survey involving 238 health professionals from private healthcare facilities in Gondar Town was conducted between March and June 2023. The study analyzed data using SPSS Version 26 and AMOS Structural Equation Modeling Version 23. Results All participants reported using social media platforms, with Facebook, Telegram, and YouTube being popular choices. Performance expectancy, social influence, facilitation condition, and behavioral intention significantly influenced healthcare professionals’ adoption of social media marketing. Performance expectancy and social influence exhibited the strongest impact on behavioral intention, acting as mediators influencing usage behavior. However, effort expectancy did not significantly influence behavioral intention. Age, gender, experience, and voluntariness showed no significant moderating effects. Conclusion This study contributes valuable insights into social media marketing adoption in the healthcare sector, emphasizing the critical role of various factors in shaping behavioral intention and usage behavior. The findings offer practical implications for private healthcare providers, policymakers, and marketers, guiding strategies to enhance patient communication and engagement through social media in Gondar Town.

BIM Roles in Enhancing Building Performance in Construction Projects Through Communication Management Case Study: The Grand Egyptian Museum

In Egypt’s construction industry, achieving successful project performance is still a challenge, particularly in construction projects involving multiple stakeholders, where the requirements increase and different management approaches between the multi-stakeholders during project phases affect the communication process. However, the reliance on traditional methods of transmitting information and communicating between all parties results in many problems. Therefore, many software and technologies are developed to improve communication management and address the challenges of complying with the information that stakeholders require. BIM technology has emerged as a suitable platform for information exchange and visualization, which can aid in increasing the quality of information transfer and exchange to ease construction management and improve building performance. Thus, this research aims to improve building performance by improving communication management in construction projects using BIM. A qualitative research methodology is adopted to achieve the aim of this research. It is based on an analytical study of a literature review, a case study on a BIM project in Egypt and semi-structured interviews carried out in the Grand Egyptian Museum project. The research outcome lies in exploring the role of BIM to achieve the most possible benefits and raise the management performance of the communication process during construction.

The Application of Big Data Technology in Teaching College Students’ Mental Health Education in Colleges and Universities

Abstract Advances in big data technology herald a new era for mental health education in higher education, offering novel solutions to age-old challenges in student psychological care. This paper investigates how big data’s analytical capabilities can surpass traditional, one-size-fits-all mental health assessments by leveraging detailed student data for personalized care. We applied data mining techniques to the mental health data of 1,200 students from College G, creating a rich database of psychological patterns and a mental health information exchange platform. The analysis led to the identification of 500 instances of psychological distress with an accuracy of 79.5% and unveiled patterns linking academic stress and adjustment difficulties to mental health issues. Our research underscores big data’s role in enhancing mental health interventions, providing the groundwork for more individualized and effective mental health services in academic settings.

Emotion Classification on Social Media Comments Using Categorical Feature Extraction Along With the Bidirectional Encoder-based Recurrent Neural Network Classification

All across the world, social media is one of the most widely used platforms for information exchange. Comments on relevant information might be made in response to a video or any other piece of information. A remark may include an emotion that may be recognized by an automated recognition system. On Facebook, Twitter, and YouTube comments, we performed studies to determine their emotional categorization. A set of comments is gathered and manually classified using six fundamental emotion labels (happy, sad, angry, surprised, disgust, and fear) and one neutral label, with each emotion label representing a different emotion category. A prominent approach in natural language processing (NLP), deep learning has been used in a wide range of categorization applications. This procedure begins by preprocessing the input data with normalization, followed by categorizing characteristics in feature extraction utilizing the Linguistic and word count analysis (LIWC). Finally, for the categorization stage, the classify features might be supplied. Finally, for categorizing emotions, the Bidirectional Encoder based recurrent neural network classification approach is used. The studies have been carried out with the use of typical social media data that has been acquired from the kaggle data repository. The findings show that the suggested model outperforms all other existing mechanisms in terms of overall performance.