## 4 Results
### Topic Modeling and Sentiment Analysis  
Topic modeling is a text mining technique commonly used to uncover the semantic components of the provided text. This statistical modeling technique is used in the NLP field, such as in sentiment analysis. In this proposed framework, the BTM topic modeling approach for short text is applied to the Mrsool dataset to discover the latent topics that affect user experience. In order to apply BTM topic modeling, the dataset underwent the necessary data preprocessing and cleaning steps. The cleaned reviews were converted into a suitable structure for BTM topic modeling. Then, different experiments were conducted to find the optimal number of topics. By using the grid search method, multiple hyperparameters such as the topic number were assigned to obtain the highest coherence score with the appropriate number of topics. Table 5 illustrates the average coherence score of Equation (5) for different topic numbers and hyperparameters, which resulted in the optimal number of topics being identified as four, with an average coherence score of \(-103.24\). Figure 6: The most used words for (**a**) positive, (**b**) negative, and (**c**) neutral sentiment in Mrsool. By using the interactive Plotly tool, the four topics are visualized in Figure 7. Notably, all the groups of topics in the presented figure are clearly separated from one another. As a result, the topics have no commonalities, and we can be confident that our proposed topic modeling is suitable for analysis. Thus, our earlier investigation revealed that the optimal number of topics in the dataset is four non-overlapping latent topics. However, BTM does not expressly provide topic names. Similar to exploratory factor analysis, it must analyze and categorize the underlying theme of each topic's keywords. Thus, the top 15 keywords were analyzed, and labels were assigned to the latent topic based on our subjective interpretation. Table 6 displays the four most frequently discussed topics in the dataset, including topic word clouds, topic descriptions, and topic names. For the first topic, the keywords were \"delivery\", \"account\", \"payment\", \"request\", \"customer\", \"money\", \"right\", \"support\", \"delegator\", \"amount\", \"complaint\", \"day\", \"I say\", \"pass\", and \"something\", which represented delivery and payment problems. Meanwhile, for the second topic, the keywords were \"customer\", \"software\", \"update\", \"delegator\", \"not very\", \"very\", \"account\", \"support\", \"problem\", \"solution\", \"communication\", \"response\", \"service\", and \"request\", representing customer support and application updates. As for the third topic, the keywords were \"price\", location\", \"request\", \"delegator\", \"customer\", \"value\", \"delivery\", \"expensive\", \"kilo\", \"store\", \"lot\", \"program\", \"something\", \"very\", \"pass\", and \"restaurant\", relating to the delivery prices. Finally, for the fourth topic, the keywords were \"request\", \"delivery\", \"dealing\", \"thanks\", \"work\", \"delegator\", \"advice\", \"service\", \"excellent\", \"program\", \"support\", \"respectable\", \"preferred\", \"customer\", and \"fast\", which generally represented users' opinions regarding the Mrsool app. Moreover, using the pie chart of the Plotly tool helped in finding the proportion of each extracted topic from the Mrsool dataset as shown in Figure 8. From the chart, the most discussed topic by users in the dataset is the application, at a percentage of 29.5%, which makes the topic of application the most important topic from the user's perspective, which should be dealt with carefully to improve user satisfaction. This is followed by the prices topic, at a percentage of 29.4%, making it the second most popular topic after the topic of application. Naturally, this topic carries a lot of weight from the users' point of view, among others that must be paid attention to.