## 4 Results
### General Analysis  
After the data preprocessing stage, the experimental dataset of Mrsool comprised 8562 user reviews with 9319 words. The top ten most used words in the Mrsool dataset were visualized using the bar chart and word cloud as presented in Figure 4a,b, respectively. For the Mrsool dataset, \"request\", \"delivery\", \"delegator\", \"price\", \"customer\", \"support\", \"program\", \"service\", \"bad\", and \"account\" are the most commonly repeated words. The repetition of these words gives an idea of the most important topics that we can find in the comments. As an example, the word \"price\" indicates the possibility of finding a latent topic affecting the user experience centered around this word. Likewise, concerning the word \"delivery\", there may be comments regarding \"delivery problems\", \"delivery speed\", \"delayed delivery\", and so forth, which can be discovered.  
The data annotation in this study offers a deeper examination of the labeled reviews using the CAMeL tool, which classified the reviews as positive, negative, or neutral. Figure 5 shows the monthly volume of reviews of the Mrsool super application based on user sentiment throughout the previously mentioned period of 2022 to 2023. This figure presents interesting data that require interpretation. There was a significant increase in the volume of daily reviews in May and July. This increment occurred with high probability due to attracting new customers to the application during summertime or due to a problem with the application that prompted users to express their opinion via writing reviews. However, this increment appeared to have started to decrease slightly by the end of the year. Although there were several negative comments, they were overshadowed by positive user opinions, which is a good sign that the application is still liked by users.  
Figure 4: The most used words in the Mrsool dataset: (**a**) word frequencies and (**b**) word cloud.  
Figure 5: Monthly number of reviews combined with users’ sentiments for Mrsool.  
Furthermore, we extracted the most frequent words for every label, namely, positive, negative, and neutral as shown in Figure 6a-c, respectively, displaying the top terms according to Mrool's dataset using word cloud visualization. Apparently, most of the positive comments revolve around the users' admiration for the application. For example, \"wonderful\", \"respectful\", \"fast\", \"thanks\", \"beautiful\", and \"excellent\" all demonstrated the users' admiration for the application. As for the negative comments, it seemed that users were angry and dissatisfied when writing words like \"bad\" and \"problem\". Meanwhile, for neutral comments, words such as \"account\" and \"communication\" may indicate the obstacles or needs that users experience in relation to the application. Markedly, there were general words that were repeated in all sentiments, such as \"request\", \"delivery\", \"delegator\", \"price\", and \"program\", which need further investigations to discover their contents.  
We observed the general statistical information of the Mrsool super application to establish an overview of the most frequent terms in all sentiments. These general analyses do not provide insights into how users feel about the services provided. For example, the words \"delivery\", \"order\", and \"price\" were repeated in all sentiments, which makes it difficult to determine whether users were satisfied or angry with the provided service. Hence, the next section of this study will explain in detail the procedures of separating different reviews according to the extracted topics, with the aim of classifying the sentiment information of each topic.