Integration of Meta-Analysis in Enhancing Machine Learning-Based Chatbot Systems: a Comprehensive Review

EasyChair Preprint 12032

Abstract

The ever-evolving landscape of chatbot systems demands continuous improvement and innovation. This comprehensive review explores the integration of meta-analysis methodologies to enhance machine learning-based chatbot systems. Meta-analysis, traditionally applied in scientific research synthesis, is employed here as a powerful tool to analyze and synthesize findings across multiple studies in the realm of chatbot development. By systematically reviewing existing literature, this paper aims to provide insights into the effectiveness of various machine learning approaches within chatbot systems, identify key challenges, and propose strategies for improvement. The integration of meta-analysis facilitates a holistic understanding of the current state of machine learning-based chatbots, offering valuable guidance for future research and development efforts. Through our meta-analysis-driven approach, we aim to provide a comprehensive understanding of machine learning chatbot systems, their capabilities, and their limitations. This research contributes to the development of more intelligent and efficient chatbot systems, ultimately improving user satisfaction and engagement. The insights gained from our meta-analysis can guide future research and development efforts in the field of machine learning-based chatbots.

Keyphrases: Chatbot Systems, Integration Strategies, NLP algorithms, Natural Language Processing, Sentiment Analysis, conversational agents, machine learning, meta-analysis, research synthesis, user experience

@booklet{EasyChair:12032,
  author    = {William Jack},
  title     = {Integration of Meta-Analysis in Enhancing Machine Learning-Based Chatbot Systems: a Comprehensive Review},
  howpublished = {EasyChair Preprint 12032},
  year      = {EasyChair, 2024}}