# Internet Wizard for Enhancing Open-Domain Question-Answering Chatbot Knowledge Base in Education
## 5 Data Analysis and Results
### Feedback Analysis of Chatbot Responses  
Based on the feedback collected through the Rating the Answer feature, it can be observed that out of 1222 chatbot responses, 821 were rated by users. Of these, 511 answers were provided for queries generated by the Related Questions feature, while 310 answers were provided for questions inputted by users. It is important to note that the Rating the Answer feature was not available for the hardcoded answers in the local database used for persona-related questions, where a keyword-matching mechanism was employed. Figure 5: Analysis of web documents used by Internet Wizard mechanism. Figure 6: Distribution of Queries in the Chatbot. Analysis of the feedback collection documents presented in Table 1 suggests that the majority of chatbot responses were sourced from Google featured snippets and knowledge graphs. Specifically, for questions generated by the Related Questions feature, 453 answers were provided from this source, compared with 238 answers provided for questions inputted by users. Similarly, for questions input by users, most answers were sourced from Google featured snippets and knowledge graphs, with only 72 coming from TF-IDF and TensorFlow. This finding indicates that Google's featured snippets and knowledge graph sections of the Internet Wizard mechanism played a significant role in providing answers. The feedback ratings for the answers were generally high, with an average rating of 4.69 for questions sourced from the Related Questions feature and Google featured snippets and knowledge graphs, and an average rating of 4.70 for questions inputted by users and sourced from the same source. Figure 7 depicts that the majority of responses sourced from Google featured snippets and knowledge graphs received a rating of 5. However, when TF-IDF and TensorFlow were used as sources of answers for user-input questions, the average feedback rating decreased to 3.81. Responses sourced from TF-IDF and TensorFlow received lower ratings compared with those sourced from Google's featured snippets and knowledge graphs, as illustrated in Figure 7, indicating a lower level of satisfaction with the answers provided by these sources. Regarding the number of records, the highest count of 453 was for answers sourced from Google featured snippets and knowledge graphs for questions generated by the Related Questions feature. The lowest count of 58 was for answers sourced from TF-IDF \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source of Query** & **Source of Answer** & **Record Count** &
\begin{tabular}{c} **Response Feedback** \\ **Rate Average** \\ \end{tabular} \\ \hline \multirow{2}{*}{Related Questions Feature} & Google Featured Snippets and & 453 & 4.69 \\ \cline{2-4}  & Knowledge Graph & 238 & 4.7 \\ \cline{2-4}  & Google Featured Snippets and & 238 & 4.7 \\ \cline{2-4}  & Knowledge Graph & 72 & 3.81 \\ \hline \hline User-input Questions & TF-IDF and TensorFlow & 58 & 4.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average feedback rate for chatbot responses by query and answer source. Figure 7: User’s feedback rating for chatbot responses. and TensorFlow for questions generated by the Related Questions feature. The average feedback rate was 4.34, indicating that the majority of users were highly satisfied with the chatbot's responses. A feedback rating of 4 or above is generally considered positive, indicative of the chatbot's ability to accurately answer a wide range of questions and provide users with the information they need satisfactorily. Overall, the results suggest that Google featured snippets and knowledge graphs were the primary sources for generating answers in the Internet Wizard mechanism. The high feedback ratings for answers sourced from this section Internet Wizard mechanism indicate that users were generally satisfied with the answers provided. Based on the feedback analysis, the Internet Wizard mechanism appears to be generally effective at answering user queries with a high level of user satisfaction.