# Identifying Emerging Issues in the Seafood Industry Based on a Text Mining Approach
## 3 Results and Discussion
### Dynamic Time Warping (DTW)  
As described in the previous section, we employ the DTW method to classify emerging issues. With DTW, we categorize keywords into similar growth trends by comparing the changes in their mention counts over time. One advantage of this model is that using statistical techniques minimizes the reliance on researchers' subjective biases in classifying numerous keywords. We normalize the data to focus on the S-shaped growth pattern of issues, instead of being influenced by extreme mention counts of particular issues (e.g., _coronavirus_). To determine the number of clusters, we use several types of internal cluster validity indices as our guidelines. Table 2 summarizes the values of these indices based on monthly data, in which higher values of Sil (Silhouette index, [28]), SF (score function, [29]), CH (Calinski-Harabasz index, [30]), and D (Dunn index, [30]) indicate better clustering method performance. In contrast, lower values of DB (Davies-Bouldin index, [30]), DB star (modified Davies-Bouldin index, [31]), and COP index (context-independent optimality and partiality properties index, [30]) indicate better performance. We conduct the same analyses by converting the monthly data into cumulative ones and the results are reported  
Figure 3: Word cloud for the top 200 words in the entire sample by country (East Asia). in Table 3. For the monthly data, we opt for four clusters, while three clusters are chosen for the cumulative data. Figure 4 shows the normalized frequency of keywords from the sample (the sample targeted the top 50 keywords with the highest frequency each year; among these, those that did not overlap by year are added and used), clustered by monthly data. The bold dashed line in each panel represents the medoid time series in the cluster. The first cluster (Periodical Issues) consists of keywords that have been seen periodically, especially before _coronavirus_ issues. The second cluster (Corona Issues) includes keywords with increasing frequencies once the _coronavirus_ issues occurred. The third cluster (Post-Corona Issues) includes the keywords with rapidly increasing frequencies after the coronavirus issues. The fourth cluster (Emerging Issue Candidates) is the group of keywords that have drawn attention recently, which can be seen as a candidate group for emerging issues. Figure 5 displays the normalized frequency of keywords from the cumulative data. The first cluster (Coronavirus Issues) refers to a set of keywords that have significantly increased in frequency due to the COVID-19 outbreak but have not increased in frequency recently. The second cluster (Issues of Constant Interest) corresponds to the set of keywords with a steady frequency throughout the sample period between 2019 and 2022. The third cluster (Issues of Recent Interest) refers to a set of keywords that did not draw much attention in the beginning but received frequent post-pandemic mentions. Table 4 lists the major keywords for each cluster from the monthly data. Cluster 1 comprises the keywords that receive regular attention, including certain types of food items, health, diet, and trade issues. In Cluster 2, with the outbreak of the pandemic, the main keywords are coronavirus and related issues such as its epicenter, similar diseases, hygiene, and prevention. In Cluster 3, coronavirus and related issues such as lockdown and logistics problems still predominate, but some other keywords related to diet and trade issues also appeared. Cluster 4 focuses on keywords that are currently receiving the most attention, which are potential candidates to become emerging issues. Similarly, Table 5 classifies the keywords in each cluster from the cumulative data. Except for the keywords related to the single coronavirus issue (Cluster 1), the keywords in Clusters 2 and 3 can be identified as emerging issue candidates. To sum up, keywords that are not limited to the pandemic issues but continue to receive attention and interest even after the pandemic can