Keyword Analysis of College Counsellor Talking Based on Big Data Technology

Abstract

College counselling sessions are being revolutionized by the integration of big data technology, offering students personalized and data-driven guidance throughout their academic journey. By leveraging big data analytics, college counsellors can access comprehensive insights into students' academic performance, interests, and aspirations. This wealth of information enables counsellors to tailor their advice and recommendations to meet the unique needs and goals of each student. Moreover, big data technology allows counsellors to identify trends and patterns in student behavior, facilitating proactive interventions to address challenges and enhance success. This paper presents a novel approach for keyword analysis of college counsellor conversations using big data technology, enhanced by Stop Feature Multi-Dimensional Stacked Deep Learning (SFMD-SDL). The proposed methodology aims to extract valuable insights from counseling sessions by analyzing keywords and patterns in counsellor-student interactions. Through simulated experiments and empirical validations, the effectiveness of the SFMD-SDL-enhanced keyword analysis approach is evaluated. Results demonstrate significant improvements in accuracy and efficiency compared to traditional methods. For example, the SFMD-SDL model achieved an average accuracy rate of 90% in identifying key keywords related to student concerns and aspirations. Additionally, the framework enabled counselors to identify emerging trends and patterns in student behavior, facilitating proactive interventions and personalized guidance. These findings underscore the potential of SFMD-SDL in enhancing the effectiveness of college counselor conversations and improving student outcomes.