## 3 Results
### Azure Text Analytics Results  
Regarding the Azure Text Analytics service, 2000 tests were conducted comprising 1000 positive and 1000 negative sentences expressing dissatisfaction. Each sentence was analyzed to determine whether it was correctly identified as positive or negative. The analysis of the positive and negative texts showed the following results:  
* True positives (_TPs_): In total, 990 of 1000 positive texts were accurately classified as positive. This high number reflects the model's strong capability to identify positive sentiment accurately. * False negatives (_FNs_): Only one positive text was incorrectly classified as negative. This low rate of false negatives suggests that the model is quite reliable in detecting positive sentiments. * True negatives (_TNs_): In total, 678 negative texts were recognized as negative. This demonstrates a solid performance in recognizing negative sentiments within the dataset. * False positives (_FPs_): Four negative texts were mistakenly classified as positive. While this is a small number, it indicates a slight tendency of the model to misclassify negative sentiments in certain cases. The sum of _TPs_, _TNs_, _FPs_, and _FNs_ was 1673 instead of 2000, as some tests were classified as neutral or mixed, and thus did not contribute to the calculation of labels as either positive or negative in any way. The analysis also reveals the distribution of mixed and neutral classifications. Among the positive texts, there were 5 mixed and 4 neutral classifications, while the negative texts showed 318 neutral classifications and no mixed sentiments. The high values of _TPs_ and _TNs_, combined with low _FPs_ and _FNs_, suggest that the Azure Text Analytics model performs exceptionally well in sentiment classification. This demonstrates its potential for deployment in applications requiring accurate sentiment analysis, such as the level identified in text messages. The four metrics of _Precision_, _Recall_, _F\({}_{1}\)-score_, and _Accuracy_, are computed as follows:  
1. _Precision_ is the ratio of correctly identified positive instances to the total number of instances identified as positive, and it is identified with a 99.59% value: \[Precision=\frac{TP}{TP+FP}=\frac{990}{990+4}=0.9959=99.59\%\]
2. _Recall_ is the proportion of correctly identified positive instances to all actual positive instances, having a value of 99.89%: \[Recall=\frac{TP}{TP+FN}=\frac{990}{990+1}=0.9989=99.89\%\]3. The \(F_{1}\)_-score_ is the harmonic mean of precision and recall, balancing the two metrics effectively. Its value is 99.73%: \[F_{1}-score=2\times\frac{Precision\times Recall}{Precision+Recall}=2\times\frac{0.995 9\times 0.9989}{0.9959+0.9989}=0.9973=99.73\%\]
4. _Accuracy_ is the ratio of correctly identified instances (positives and negatives) to total cases. In total, 90% of the sentences were correctly classified: \[Accuracy=\frac{TP+TN}{TP+TN+FP+FN}=\frac{990+678}{990+678+4+1}=0.9970=99.7\%\]  
These metrics reflect a high overall performance in reporting an extended number of sentences. The combined effectiveness of the WBM classification model and the Azure Text Analytics service has proven instrumental in enabling the accurate calculation of the _EGI_. The WBM model's precision and adaptability, particularly in analyzing complex datasets involving the _WBI_ and _DSC_, ensured performance across diverse scenarios. The integration of the Azure Text Analytics service, which demonstrated a 99.7% accuracy in detecting negative sentiments and a 99.59% precision, further contributed to the reliability of the _EGI_ calculation. The _EGI_ calculation captures a comprehensive view of well-being and highlights key areas of dissatisfaction, allowing for more nuanced insights. This superior classification and sentiment analysis performance demonstrate the strength of combining cutting-edge ML models with advanced text analytics services.