# Developing the NLP-QFD Model to Discover Key Success Factors of Short Videos on Social Media
## 3 Methodology  
This study proposed an NLP-QFD model to identify the key success factors (KSFs) of short videos. The detailed steps included in this model could be divided into 7 steps. An implemental flowchart has been provided in Figure 1. The concise steps are provided below. Step 1: Data Collection  
This study focuses on short videos, including their contents and reviews. Specifically, it concentrates on YouTube Shorts, collecting video content, descriptions, and audience comments from platforms such as CNN-YouTube and Middle East Eye-YouTube. The YouTube Data API v3 will be used as a web crawler tool to extract comments, video content, descriptions, social media links, audience feedback, and other relevant factors. YouTube Shorts is chosen as the primary focus due to its widespread popularity, and CNN-YouTube and Middle East Eye-YouTube are selected as data sources to analyze events from both official media and user-generated content perspectives through audience comments. Step 2. Transform audio data into text data  
In order to compare the video content to social media users' comments, the videos include audiovisual elements. Therefore, based on audio, the collected video content will be transcribed into text format using Just Recap ([[https://recap.it/](https://recap.it/)]([https://recap.it/](https://recap.it/)) accessed on 1 March 2024) for further analysis and comparison. Step 3. Data pre-processing and natural language processing  
We used Python to pre-process the collected text data. The pre-processing phase includes several steps, such as tokenization and lemmatization, before obtaining the TDM for LDA analysis. Natural Language Processing (NLP) takes into account intriguing text mining applications, and this method is qualified to extract pre-existing knowledge from the text. The complex sentences that occasionally appear in social media user-generated content with the intention of objective communication can be significantly extracted using NLP. The implementation process can be summarized as follows [19; 36]. Step 3.1: Tokenization  
The Python Natural Language Toolkit (NLTK) has been used for text tokenization. \"Uni-gram\" has been used to segment sentences. Step 3.2: Data cleaning  
In this step, we remove stop words such as \"and\" and \"or\", non-English words such as those in Arabic or Hebrew, and some meaningless icons to clean collected data. Step 3.3: Lemmatization  
The aim of this step is to reduce complex forms of a single word to their most basic form, such as \"wrote\" and \"written\" to \"write\". Step 3.4: Calculate term frequency  
A word frequency count has been performed and the terms with a frequency of less than 10 will be removed. Step 3.5: Construct Term-Document Matrix (TDM)  
In this step, we create a Term-Document Matrix (TDM) using the TF-IDF (term frequency-inverse document frequency) weights shown in Equation (1). \[TF-IDF=TF(t_{i}\,,d_{i})\times\log\biggl{(}\ \frac{N}{N(t_{i})}\ \biggr{)} \tag{1}\]  
Figure 1: Implemental procedure of this study. where \(t_{i}\) means the ith term; \(d_{j}\) represents the jth document; N is the total number of all documents; N (\(t_{i}\)) denotes the number of documents which contain \(t_{i}\) features. Step 4: Topic Extraction  
Latent Dirichlet Allocation (LDA) is a machine learning technique used for topic modeling and is capable of discovering the thematic structure within a collection of texts. In this study, LDA will aid in exploring the latent themes and patterns within video content, descriptions, and audience comments. The steps for executing LDA include text preprocessing, setting LDA model parameters, fitting the LDA model, and interpreting and analyzing the results to understand the thematic structure and underlying patterns within the text data. In this study, the coherence score has been employed to determine the number of selected topics in the LDA model. Next, the terms with high loadings will be considered as keywords in the selected topics.