## 3 Proposed Methodology
### New Approach to Topic Modeling  
Transformer-based zero-shot classification can predict to which degree an input text belongs to which label without training a new classification model [50]. Therefore, any clustering algorithm could identify the topics and assign the text to the topics using zero-shot classification. To reduce noise, it is proposed that DBSCAN be used for identifying topics. After extracting the key phrases, the DBSCAN algorithm is applied to the vectorized key phrases to group them into topics. The DBSCAN algorithm was chosen due to its efficiency with extensive data collections. It identifies and eliminates noise instead of adding it to the nearest topic. It has only two parameters that need to be adjusted to improve the result: a distance epsilon and a minimum number of points that can make up a cluster [51]. A heuristic can be applied to determine a valid combination of the two parameters. The clustering result is acceptable when the noise cluster contains no more than 30% of all data elements and the most prominent cluster has 50% of all data elements [52]. The algorithm used for adjusting the parameters of the DBSCAN is based on the heuristic proposed in [52]. The start parameters chosen for MinPoints are four and twice the dimensions of each datapoint minus one, as suggested in [52]. Starting with four on the lower end adds to the heuristic proposed in [52]. Evaluating the potential MinPoints from both perspectives, i.e., a high and a low starting value, facilitates a more expedient determination of optimal parameters. For each of these MinPoints values, a nearest-neighbor plot is generated. This plot can differ depending on the dataset. The plot pictures the distance of each point to its k-nearest-neighbor. The y-axis represents the distance from the point to its k-nearest-neighbor. The x-axis shows the index of data points ordered from those with the highest distance to those with the lowest distance. The authors suggest the following for finding parameters matching the abovementioned criteria: The nearest-neighbor plots are generated with the start parameters chosen for MinPoints. The y-intercept of the elbows point in the plot is used as the epsilon parameter. If the quality criteria are not met with the resulting clusters, the lower start value for the MinPoints parameter is doubled, the higher start value is halved, and the clustering is done again. The described procedure is repeated until the criteria are met. If the criteria are not met, the algorithm is stopped when the repeatedly doubled low value for MinPoints becomes higher than the repeatedly halved high value for MinPoints. In that case, the MinPoints value with the best values for the quality criteria is chosen. Then the epsilon parameter can be readjusted using this value for the MinPoints parameter. An automation search of these parameters based on our process is partly possible. The MinPoints parameter could easily be chosen automatically with the algorithm described above. The epsilon parameter is more complex since the resulting plots differ from dataset to dataset. However, this is not part of this study and could be done in future work. The resulting clusters are then analyzed in a manual reading process and grouped into sensible topics based on a human assessment. The zero-shot classification model returns a confidence score expressing the confidence with which it assigned a topic to a sentence. The confidence for a label decreases with an increasing number of labels used as input for the model when the multi-label option is not used. When using the multi-label option, the result was that some labels were overrepresented. Assessing the labels in the Echo Dot dataset, especially the smart-home label, returned high confidence for most sentences. Therefore, the usage of zero-shot classification without a multi-label option is suggested. A high number of labels for one zero-shot classification pipeline without multi-label is not recommended due to decreasing confidence.