Optimizing Chatbot Effectiveness through Advanced Syntactic Analysis: A Comprehensive Study in Natural Language Processing

Abstract

In the era of digitalization, the interaction between humans and machines, particularly in Natural Language Processing, has gained crucial importance. This study focuses on improving the effectiveness and accuracy of chatbots based on Natural Language Processing. Challenges such as the variability of human language and high user expectations are addressed, analyzing critical aspects such as grammatical structure, keywords, and contextual factors, with a particular emphasis on syntactic structure. An optimized chatbot model that considers explicit content and the user’s underlying context and intentions is proposed using machine learning techniques. This approach reveals that specific features, such as syntactic structure and keywords, are critical to the accuracy of chatbots. The results show that the proposed model adapts to different linguistic contexts and offers coherent and relevant answers in real-world situations. Furthermore, user satisfaction with this advanced model exceeds traditional models, aligning with expectations of more natural and humanized interactions. This study demonstrates the feasibility of improving chatbot–user interaction through advanced syntactic analysis. It highlights the need for continued research and development in this field to achieve significant advances in human–computer interaction.

A Bibliometric Analysis of Text Mining: Exploring the Use of Natural Language Processing in Social Media Research

“…Although it started from simple approaches such as morphological analysis, nowadays, with the help of techniques based on machine learning, it manages to learn complex models, perform sentiment analysis, translate text automatically, provide virtual assistance, build chatbots, extract data, provide summaries, and perform numerous other tasks. The progress in this area has been synthesized in various studies from the field, such as those by Ortiz-Garces et al [7], Chang [8], Hirschberg [9], Zhang et al [10], Jiang et al [11], and more.…”