## 5 Results  
This section focuses on clarifying the results of this research's evaluation from two points of view: ground truth and qualitative analysis.  
The quality and performance of the interest topics that were inferred for each user were determined using a ground truth table with hamming loss, precision, recall, f1-score, accuracy, and match score are essential aspects. Moreover, ground truth is evaluated by measuring the inferred interest extracted by LDA against actual interest obtained from users' tweets and biography. This research used hamming loss, precision, recall, f1-score, accuracy, and match score to evaluate the multi-class and multi-label data. The hamming loss consequential method for evaluating the multi-class and multi-label data, which is the number of incorrect label predictions, is calculated as:  
\[HL=\frac{1}{NL}\sum_{l=1}^{L}\sum_{i=1}^{N}y_{i,l}\oplus\ X_{i,l} \tag{5}\]  
where \(X_{i,l}\) is a Boolean value that indicates whether the \(i\)-th predicted the \(l\)-th label and \(\oplus\) denotes exclusive OR.  
Precision refers to the number of the outcomes graded as positive were truly positive and recall refers to the number of true positives recalled while F-measure depends on precision and recall. To measure the accuracy, two paramount matrixes must be calculated: sensitivity and specificity. The metric that calculates an ability to estimate true positives in each available class is called sensitivity. The metric that measures ability to anticipate true negatives is called specificity. Accuracy depends on sensitivity and specificity, while match score indicates the percentage of correct predictions over true labels. All measures used for evaluation are shown in Table 10.  
The results of hamming loss, precision, recall, f1-score, accuracy, and match score are shown in Table 11.  
The inferred process's efficiency was high; precision indicates how valid the interests extracted are, while recall refers to the ability to infer a large portion of the user's interests. It is of note that the f1 score thread contains both precision and recall, and that the accuracy percentage for topics predicted successfully as interest and properly categorised as not of interest is over the maximum number of topics of user interest. Moreover, the match score indicates the percentage of correct interest over true labels, whereas hamming loss indicates the number of incorrectly labelled interest predictions.  
\begin{table}
\begin{tabular}{c c} \hline
**Measure** & **Mathematical Formula** \\ \hline Precision & _True positive_ \\  Recall & _True positive_ \\  F-measure & _2-precision_ \\  Sensitivity & _True positive_ \\  Specificity & _True Negative_ \\  Accuracy & _True positive_ +_false positive_ \\  Match score & _True positive_ +_true Negative_ \\ \hline \end{tabular}
\end{table}
Table 10: Measures used for evaluation.  
The above table shows that the user interests are extracted with high accuracy of 85%, which means that it extracted many of the user's interests correctly.  
In the quantitative analysis we present an analytical study of a group of users in the dataset to study similarities and differences. As a way to verify the validity of the claims presented in this research, three factors will be assessed: the big five personality traits, demographic data, and interest topics. The first to be verified is the big five personality traits factors independently, followed by the demographic data alone. The following stage considered user similarities from the perspective of their interests. Finally, the similarity was analysed based on all three factors together. All possible cases are, thus, covered in this qualitative analysis as follow:  
* Case 1: Similar Personality, Demographics Data and Interests
* Case 2: Similar Personality but Different in Demographics Data (Age), and Interests
* Case 3: Similar Personality and Different Demographics Data (Age and Gender) and Interests
* Case 4: Different Personality, Interests and Similar Demographics Data (Age and Gender)