# Marketing Insights from Reviews Using Topic Modeling with BERTopic and Deep Clustering Network
## 6 Results  
### Extracting Features  
The performance of the models was evaluated using three metrics. Topic coherence is an evaluation metric derived from the assumption that better-performing topic models will result in extracted keywords for each topic that have semantically similar meanings. In this experiment, we used NPMI (Normalized Pointwise Mutual Information) [28] and Word Embedding-based Topic Coherence. NPMI quantifies how often two words appear together in a text. It ranges from \(-1\) to \(1\), with higher values indicating better coherence. Word Embedding Coherence (WE), previously mentioned in Section 4.4, is an evaluation metric that involves embedding the extracted keywords as vectors and calculating the similarity between words. A higher value indicates a higher semantic similarity among keywords within the topic. Topic Diversity (TD) is an evaluation metric that assesses how distinct the extracted topics are from each other in terms of meaning [29]. It measures how different semantically meaningful keywords have been extracted among different topics. It is calculated as the proportion of unique words across all topics. It ranges from 0 to 1, with values closer to 1 indicating a higher diversity. The results of the topic modeling for a randomly selected product, which is detergent, are presented in Figure 4.  
The left side of each graph represents the result evaluation metric for extracting advantages, while the right side represents the evaluation metric for extracting disadvantages. The extraction of disadvantages received lower scores compared to the extraction of advantages in terms of performance. This is likely due to the difference in dataset sizes. Generally, the number of reviews with low ratings is significantly lower than those with high ratings in consumer-generated reviews. Therefore, the amount of data used for extracting disadvantages was much smaller in the collected dataset, which is reflected in the results. When comparing the performance among the models, the more recent algorithms using the DCN and BERTopic generally outperformed traditional algorithms. Between the DCN and BERTopic, BERTopic showed slightly higher scores. Particularly, BERTopic exhibited the most outstanding performance in terms of Topic Diversity among the four models. However, for the word embedding metric, which was emphasized in this study, the DCN also demonstrated a good level of performance comparable to BERTopic. An example of extracted features of a product is shown in Table 1. The keywords in the table below were extracted from two or more of the various models, and the ones that have marketing significance were selected. The original topic modeling results can be found in Appendix A. The selected product is a mold remover detergent. We can see that keywords related to the product include'mold', 'cleaning', 'odor', and'removal'. From the table, it can be inferred that the product has a good effect on mold removal but has an intense odor, which seems to be its major drawback. If devising a sales strategy for this product or a similar one, efforts could be directed towards mitigating the odor issue or emphasizing its effective mold removal capabilities by crafting advertisements. Figure 4: Topic modeling evaluation. (**a**): NPMI, (**b**): Topic Diversity, (**c**): WE Coherence. KcBET + UMAP + HDBSCAN equals the basic BERTopic. Furthermore, in the case of the LDA and K-means methods, which exhibited relatively low topic diversity values, the occurrence of words such as'mold','spray', 'use', and'removal' across multiple topics with overlaps can be observed. On the contrary, for the DCN-based model and BERTopic, which had relatively high topic diversity values, there were fewer overlapping words between different topics and a greater diversity of extracted keywords. This could potentially help reduce overlooked information in consumer reviews and yield more insights. In terms of topic coherence, higher values facilitated the comprehension of what content described the entire topic.