## 5 Results and Discussion
### Discussion
#### 5.2.2 Key Improvements to Requirements Elicitation  
With our method, this issue is resolved. It is possible to assign one document to several topics, giving a more realistic representation of the mentioned topics. It is also helpful to understand which topics are mentioned together, though this functionality is not implemented in the UI yet.  
The extracted user needs show that the proposed algorithm enables users without an information technology background to generate insights from big data with low time investment. The developed UI is a valuable tool for maintaining the connection between topics, summaries, and reviews. It facilitates a better understanding of the results and allows users to perform quality checks by delving into the reviews. The functionality to add and remove topics from the opportunity chart changed the importance, satisfaction, and opportunity of the remaining topics since these values are normalized. Therefore, altering the topics in the opportunity matrix could lead to new, underserved topics. Although there is room for improvement in interface performance and design and the tool has proven indispensable for gathering insights on each identified topic. Without it, retrieving the detailed results of the case study would not have been possible.  
During the development of the algorithm, we implemented different language-transformer technologies for the first time in requirements elicitation. We used transformer-generated key phrases for topic modeling. The topic modeling is suggested with DBSCAN to filter the noise in the key phrases. These topics are manually filtered and grouped before zero-shot classification is used to calculate the contribution scores of the topics to the documents. The proposed algorithm adds, with the transformer-based summarization, a completely new idea to requirements elicitation by building summaries from all sentences which have a higher contribution score in zero-shot classification than the defined threshold (0.5 for our case study), and the most relevant information of this topic is extracted in human-readable form. Due to the limitation of input token length to the transformer models, several summarizations need to be done. To present a fast overview, our algorithm suggests summarizing the summaries until there are only ten short summaries so that the human requirements engineer can start with a brief overview of the written texts on this topic.  
A lower aggregation level could be used if more information is needed and more summaries are displayed. As the lowest aggregation level, the sentences should be displayed so that the original written sentences, which are not altered by language transformers, can be read. This summarization adds more insights into the identified topics and their opportunities as formerly known algorithms for requirements elicitation. Considering the success factors (SFs) for requirements change management analyzed in [43], the proposed methodology improves the three top SFs. The most essential SF is changing acceptability. It means the product's quality depends on satisfying customer needs and expectations. The proposed solution helps to identify the customer needs and expectations from customer reviews. The second SF is updated requirements. All requirements should be updated since the customers' needs are evolving, and the complete market should also be analyzed. When the proposed methodology is executed periodically, the requirements could be built on the timely needs of the customers. As shown in the case study, it is possible to add topics from competitors to analyze the comparisons with competitors. The third SF, sharing information, could also be improved when the solution is adjusted to this need. Sharing information on existing customer needs is possible if everyone can access the proposedweb surface. However, sharing thoughts and ideas to satisfy them is currently not possible. Adding a comment and networking component to the front end could contribute strongly to this SF.