## 4 Discussion
The analysis techniques used also yielded promising results regarding the multifaceted nature of NLP. While Decision Trees and logistic regression provided valuable insights into the importance and weight of features, the permutation technique highlighted the fragility and interdependence of these features. Changing a single feature, such as syntactic structure, could have a ripple effect on the accuracy and effectiveness of the chatbot [42; 43]. Although our findings are robust within the defined context, they may not be generalized to all chatbot scenarios or applications. Additionally, the NLP field is constantly evolving, and what is true today may not be true tomorrow. Although our findings are robust within the defined environment, they may not be generalizable to all chatbot scenarios or applications. This limitation arises from the diversity of human language and the varied ways dialogues can be structured in different applications. Our results are based on a specific set of data and model parameters. Although we have attempted to cover various scenarios, the findings may not apply universally to all chatbot applications. The diversity of human language and variability in dialogue structures represent a significant challenge to generalization. The effectiveness of our chatbot model depends closely on the quality and nature of the data set used. Chatbots developed for different domains or languages may require substantial adjustments, which was not fully addressed in our study [44]. We believe our findings are most directly applicable to chatbots designed for customer service. Accurate interpretation of queries and generating relevant responses are paramount in these contexts. Since our study primarily focused on Spanish and English data, the results are particularly relevant for chatbots operating in these languages. The models may need adaptations for languages with different syntactic structures. To address these limitations and improve the external validity of our study, we propose that conducting additional studies in various domains and with different languages is crucial to evaluate and enhance the generalizability of the findings. Furthermore, it is essential to explore the adaptability of models to different linguistic and cultural structures, which is vital for creating universally effective chatbots. Based on the reviewed works, it is crucial to mention that while our results largely align with the existing literature, there are discrepancies. Some research has found that characteristics we consider secondary, such as time of day, significantly impact specific contexts. This underscores the importance of considering domain specificity when designing and evaluating chatbots. When approaching demographic data analysis in chatbot development, it is essential to consider ethical and privacy aspects carefully. The collection and use of demographic data must be carried out responsibly, ensuring that users' privacy and individual rights are protected. For this, it is crucial to obtain informed consent from users before collecting and analyzing their data. This includes transparency about data use and ensuring users know their rights. Data must be anonymized, and appropriate security measures must be implemented to protect users' personal information. It is essential to ensure that the identity of individuals cannot be revealed through the data collected. Demographic data should be used only for the specified purposes and in a manner that does not promote bias or discrimination. The interpretation and application of these data must be carried out with cultural and social sensitivity.