## 3 Proposed Methodology
### Visualization and Working with the Results  
The last step of the proposed approach is the visualization and presentation of the results. It is done in an interactive user interface (UI) to combine all the results of the three previous steps. A Streamlit app is suggested because this Python framework runs as scriptsare implemented. Therefore, the previously scripted steps could turn into a UI without expending much development effort. The opportunity chart is the central object of the UI. When hovering over the bubble representing the topics, the details of this topic are displayed. A screenshot of this feature is shown in Figure 3. This figure is created by using the Python library plotly. The related summaries on the highest aggregation level display when the user clicks on a topic bubble. If the information is too highly aggregated, the user can display a lower aggregation level or the original sentences as the lowest aggregation level. This UI element is displayed in Figure 4. The top tabs represent the aggregation levels. The summary consists of different sentences with different sentiments. For a better overview of the sentences, the background color is adjusted to the mean sentiment of all sentences aggregated in this summarization. The color bar on top of Figure 4 shows that bright green indicates the most positive sentiment and solid red indicates the most negative sentiment. If the sentiment reads all positive, but the background color indicates a more neutral or negative sentiment, it is advised to read the lower aggregated summaries. If the user evaluates one topic as irrelevant or poorly predicted by the zero-shot classifier, meaning the selected sentences are mostly not about the topic, removing the topic from the opportunity chart is possible by removing it from the multi-select box. This box is displayed in Figure 5. After removing a topic, it can be added later using this box. The importance, satisfaction, and opportunity must be recalculated when the topics have changed since the values are normalized [25] and may change with the selected topics. Algorithm 2 takes this into account and gives an optimized solution for this. It saves the values for each topic before normalizing them so that those base values do not need to be recalculated, but only the normalization needs to be done again. Finally, the boundaries between served-right, overserved, and underserved may change because the mean value of importance and satisfaction determines them. It could lead to new underserved topics with a higher opportunity worth investigating. The proposed UI enables the user to identify and investigate the underserved topics with the highest opportunity using summaries. If the topics are invalid, they should be removed, and the next opportunity should be checked. When the prioritized topics have been selected, the human creative process of requirements engineering [57; 58] starts with the acquired information from the presented process. This process should start with underserved topics and topics with the highest opportunity. As input, the user should take the positive and negative sentences from the topic so that human creativity can focus on requirements that reduce unwelcome product  
Figure 3: Interactive opportunity chart in Streamlit application. overall 'rating' is also not of interest because the goal is to find out what users are passionate enough about to write about, be it positively or negatively. In preprocessing, only comments containing three words with solely alphabetical characters are kept, and emojis are removed. Afterwards, the language is detected using spaCy and the library spacy_langdetect. The comments are filtered to those which were detected as English reviews. These reviews are split into sentences using the nltk library, which enables the zero-shot classification pipeline and sentiment analysis. After preprocessing, the next step toward topics is keyword extraction. The chosen model is multi-qa-mpnet-base-dot-v1 due to its semantic search performance [59].