# How to Extract Meaningful Insights from UGC:
## 2 Methodology and Research Questions
### Knowledge-Based Method to Extract Insights from UGC  
To make sense of the content generated by users, a methodology proposed by [PERSON] and [PERSON] [2] was applied to the data. This process uses three different approaches based on data mining and topic modeling. The first step is the development of a mathematical model to detect topics from a series of inputs on which this algorithm is applied. [PERSON] and [PERSON] [44] created the model, and [PERSON] et al. improved it [45]. The improved model is known as LDA. The model has been successfully used in various studies before [46; 47]. The objective of the model is to identify the number of times a word is repeated in a sample. Sometimes this sample can be represented by phrases or by documents [46]. The mathematical model developed in Python applies the observation of variables including the number of documents or databases on which the analysis will be carried out; these are the latent variables [44]. The variables then are used to determine the number of topics that the algorithm will find based on the importance of the total database [46]. Once the algorithm has detected the total number of words and the number of repeated words, the researcher must give names or labels to the topics [41]. This name is specified by the researcher and can be based on a phrase creation using the last words that are repeated in the analysis of a document or a database after applying the LDA model. This is an open coding method which is routinely used in research studies based on grounded theory [42]. On LDA results we apply a sentiment analysis, in this case one developed in Python that works with machine learning and that must be trained through a process in which the different samples are classified [47; 48] by the researchers; here, tweets from Twitter are classified as positive, negative, and neutral [49] manually. This sentiment-centered analysis can identify connotations and feelings with respect to the subject of study. The SA methodology aims to classify the feelings expressed in a sample. That is, using SA, researchers can analyze the feelings within a community regarding a specific theme. This analysis is based on studying the set of connotations in texts that compose the sample [50]. To determine the number of iterations required to train this algorithm so that it is ready to be used, we focus on the resulting KAV measurement [50]. This measure indicates what the level of significance is in terms of machine learning success [33; 34]. Note that in applying the threshold levels of KAV, the results must be equal to or greater than 0.667 to have an average of success that is minimally reliable [50]. A good measure that allows conclusions to be analyzed with confidence that they are meaningful would be to set the acceptable KAV equal to or greater than 0.800 [34]. In the present study, the KAV value is applied to support vector machine (SVM) algorithms. This type of trainable algorithm has an easy application for other academics, and researchers in the educational sector. The SA algorithms that work with SVMs make it possible to train SVMs with a sample (in this case, a collection of tweets), indicating through a user interface whether the sample is negative, positive, or neutral. This training is performed until the model's ability to succeed, as indicated by the KAV, is sufficient [2]. As indicated by [PERSON] [51] and replicated by [PERSON] et al. [38], the basic formula for KAV is the relationship characterized by observed disagreement/expected disagreement. The calculation method is computationally very complex, the process involves resampling methods such as bootstrapping [51]. Therefore, the proposed approach is a data analysis technique that should be useful to those who seek to use UGC to improve their communication or marketing strategies. Also, it should be useful to educational institutions that may be able to enhance their offerings by identifying needs and trends based on technologies previously investigated [52; 53; 54; 55].