## 2 Materials and Methods
### Analysis of Important Features
#### 3.4.1 Identification of Characteristics  
Correct identification of features is the first and fundamental step in any modeling process. These features act as the input to the model, and their quality can directly affect the accuracy and usefulness of the resulting model. In the context of chatbots and NLP-based models, features focus on the nature and structure of textual data. Still, they can also include contextual information that can influence the interpretation of a request or question.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Implementation Context** & **Case Description** & **Result** \\ \hline \multirow{3}{*}{Successful} & Customer Service of & Implementation of a chatbot based & Reduction in response time by 70\%, \\  & a Company. & on our model to resolve & reduction in queries to human agents \\  & a Company. & standard queries. & by 40\%, and 85\% customer satisfaction. \\ \multirow{3}{*}{Successful} & Integration with IoT & We are using the model to & \\  & Systems in a Smart City & provide real-time information on & 93\% effectiveness in correct answers. \\ \multirow{3}{*}{Successful} & Virtual Assistance for & Chatbot to help users select and & \\  & Restaurant Reservations & book a restaurant based on reviews & 50\% increase in reservations made and \\  & & and availability. & 80\% positive feedback. \\ \hline \multirow{2}{*}{Not successful} & \multirow{2}{*}{Electronics E-commerce} & Implementation of an e-commerce & Inaccurate or irrelevant responses in \\  & & site specialized in electronic products. & 25\% of interactions. \\ \hline \multirow{2}{*}{Not successful} & Intelligent Building & Integration to adjust lighting, & \\  & Management System & temperature, and other systems using & 20\% of commands are incorrect due to \\  & & voice commands. & the diversity of accents and slang. \\ \hline \multirow{2}{*}{Not successful} & Teaching Portfolio & Chatbot designed to advise & Difficulties interpreting specific \\  & Management Assistant & on academic methods and & academic terms and providing accurate \\ \multirow{2}{*}{} & student portfolios. & recommendations 30\% of the time. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of use cases: successful and unsuccessful implementations of the model.  
Textual Data:
* Syntactic Structure: Refers to how words are organized in sentences. Aspects such as word order, verb conjugation, and sentence structure can be vital for understanding the text.
* Semantics: Keywords, synonyms, antonyms, and the general context of the text are crucial. A chatbot needs to understand not only the individual words but also the meaning behind them.
* Named Entities: identifying proper names, places, dates, and other specific data is essential, especially in customer service chatbots or those that require specific actions based on details. Contextual Data:
* User Behavior History: Understanding a user's past interactions can be crucial. If a user has asked similar questions in the past or shown specific behavioral patterns, this can influence the chatbot's responses.
* Demographic Data: although not always available, aspects such as age, gender, and geographic location, among others, can help personalize responses.
* Time and Date: The time of day or specific date can change the context of a request. For example, a chatbot on an e-commerce website might interpret a query about \"discounts\" differently during a sales period. Interaction Data:
* User Sentiment: evaluating whether the user's answers or questions are positive, negative, or neutral can influence how a chatbot responds.
* Device Type and Communication Channel: whether a user interacts via a mobile or desktop device or uses live chat, email, or social media, can change how a chatbot processes and responds.  
These features form the foundation on which NLP-based chatbot models are built and trained. Proper identification and optimization of these features are essential to maximize the efficiency and accuracy of the resulting model.