# Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media
## 4 Results
### Results of LDA  
#### 4.2.1 Authentic Voices behind News Short Videos  
In ensuring the determination of the optimal number of topics, we employed the coherence score as a consistent indicator. In natural language processing, the coherence score of an LDA topic model serves as a metric to evaluate the coherence and consistency of the topics generated by the model. The coherence score measures the relevance among different words within the same topic, which ideally should co-occur within a topic. A higher coherence score indicates that the words within the topic are more correlated and coherent, whereas a lower score suggests that the words within the topic are more disparate and unrelated. Therefore, by calculating the coherence score, we can assess the quality of the LDA topic model and compare the performance of the model across different numbers of topics. Figures 4a and 4b, respectively, depict the Coherence scores of the CNN and Middle East Eye cases under different numbers of topics. From the graphs, we observe that the highest Coherence scores are achieved when the number of topics is 6 and 7, indicating that, in terms of audience textual comments, the optimal number of topics for both cases is 6 and 7, respectively. Figures 5 and 6 present implicit Dirichlet distributions of audience comments, enabling us to label these topics accordingly. Figure 3: Some examples of short videos from CNN YouTube. Figure 2: Some examples of short videos from Middle East Eye YouTube. Figure 4: Coherence score in audience’s reviews. (a) CNN case; (b) Middle East Eye. Figure 5: The Dirichlet distribution of audience comments (CNN-YouTube). Figure 6: The Dirichlet distribution of audience comments (Middle East Eye-YouTube). In Figures 5 and 6, the left-hand side is a Matrix Data Analysis Diagram, which is a visual representation used in data analysis to display relationships between different variables within a dataset. It typically presents data in a tabular format. This type of diagram can be particularly useful for identifying patterns, trends, or dependencies in large datasets and for conducting various types of statistical analyses, such as correlation analysis or factor analysis. In this study, we can use the Matrix Data Analysis Diagram to understand the relationships among extracted concepts. In Figure 5, we can see that topics #3 and #5 have overlaps when the selected seven topics are relatively independent. In addition, the right-hand side of Figures 5 and 6 summarizes the top 30 most salient terms. In the audience comments for CNN (Figure 5), we can observe that besides place names such as Israel and Palestine, the most frequently occurring words are Child, Kid, People, God, etc. This indicates a concern for the local people and children amidst the conflict in Palestine. In Figure 6, for Middle East Eye, the most frequent words in the audience comments include Child, as well as west, world, stop, and old, suggesting voices primarily from those affected by the war. Next, the extracted topics should be named. Table 2 lists representative words in six selected topics from the CNN short videos. These six topics are named as follows. This topic includes keywords such as humanity, animals, rights, terrorists, southern, Africa, etc. It mainly discusses the basic human rights in Palestinian and Israeli-occupied territories, including aid from southern African nations. Topic #3: Israeli Incursion and Hamas Counteraction  
Keywords in this topic include freedom, rights, stop, West, ongoing, Nazi, stone, Hamas, arrest, etc. It primarily discusses Israeli incursions and both support for and opposition to Hamas' actions, asserting the rights of Gaza residents and the need to uphold their freedom. Topic #4: Accusations of Genocide  
Keywords in this topic include [PERSON], immoral, barbaric, crimes, evil, genocide, massacre, and [PERSON], focusing on various accusations of genocide against Israel.