## 1 Introduction  
In the era of digitalization and connectivity, human interactions with computer systems have become ubiquitous. From conducting online searches to interacting with virtual assistants, the interface between humans and machines has undergone a radical transformation in recent decades [1]. In this context, the Natural Language Processing (NLP) domain emerges, which seeks to simulate human understanding of language and allows machines to interpret, process, and respond to human communication coherently and meaningfully [2, 3].  
Early iterations of interactive systems, such as chatbots, relied on predefined rules and manually coded responses. These systems were rigid and lacked the flexibility to handle queries that deviated from predefined scenarios. However, with the advent of NLP, supported by machine learning algorithms and large data sets, the possibility arose of developing more advanced and contextual automatic response systems. NLP has found applications in numerous fields: machine translation, sentiment analysis, recommendation systems, and chatbots [4]. Unlike their rule-based predecessors, these NLP-based chatbots can learn from past interactions, adapt to different contexts, and provide more accurate and humanized responses [5].  
Despite significant advances in the field of NLP, inherent challenges remain. One of the main problems is the variability and ambiguity of human language. People often use slang, sarcasm, metaphors, and cultural expressions that can be difficult for a machine to interpret. Furthermore, user expectations towards chatbots have increased. It is no longer enough to give a correct answer; it must be given promptly, in context, and often with a human touch [6]. The central problem that this study seeks to address is how to improve the efficiency and accuracy of NLP-based chatbots, considering the complexity and variability of human language and how to ensure responses meet the increasing expectations of users. Therefore, in NLP, syntactic elements play a fundamental role in the functioning of chatbots. These systems, designed to simulate human interaction, rely heavily on their ability to understand and manipulate the linguistic structure of sentences. The effectiveness of a chatbot is measured not only by its ability to recognize keywords but also by its ability to analyze and respond to the syntactic complexities of human language.  
The solution proposed in this study involves a multifaceted approach. First, an in-depth analysis of NLP and the key characteristics that influence the effectiveness of a chatbot is performed [7]. This includes the grammatical structure of the sentences, keywords, message length, and other contextual factors such as time of day and history of previous interactions. Through advanced machine learning techniques and feature analysis, a chatbot model is proposed that responds to queries based on explicit content and considers the user's underlying context and intentions. Extensive testing and validation ensure that the proposed chatbot outperforms traditional models regarding accuracy, contextualization, and user satisfaction [8].  
The study reveals several significant findings; firstly, it is confirmed that certain features, such as syntactic structure and keywords, play a crucial role in the accuracy of the chatbot. By optimizing the model and taking these characteristics into account, a significant improvement is achieved in the consistency and relevance of the chatbot's responses [9; 10]. Furthermore, the proposed model is adaptable to different contexts and linguistic variations, making it especially effective in real-world scenarios where language ambiguity and variability are common. In user satisfaction, the proposed NLP-based chatbot consistently outperforms traditional models, indicating that it can understand and respond to queries effectively and meets users' growing expectations regarding natural and humanized interactions.