## 5 Discussion  
The participants revealed a relatively high perceived trust score for LDA processing low affective text. This shows that the LDA algorithm is effective in extracting the topics of text with a low affective level that mainly consists of terms indicating objective information or facts. According to related research [35], we note that SA is used to summarize the characteristics of paragraphs that mainly include subjective opinions or affective expressions, such as highly affective paragraphs. In the study by [PERSON] [36], SA was used to analyze review texts corresponding to specific writing interests such as automobiles, movies, and travel. In [PERSON]'s study, the sentiment orientation of the text was determined based on the amount of information regarding the words used with apparent affective vocabulary, such as \"excellent\" or \"poor\". As result, the more emotional vocabularies that are contained in text, the sentiment orientation has a greater value. However, in this study, we observed no statistically significant difference in the perceived trust of LDA+SA with respect to the affective level. A plausible reason for this result is that there are differences in the text attributes used across studies. The results of keywords extraction through LDA+SA are basically affected by the attributes of the text itself to be analyzed. Therefore, the vocabulary represented by the attributes of text (papers, magazines, newspaper articles, etc.) may vary, even if the text indicates the same subject. This study used the university entrance examination texts as a case study to investigate the effect of LDA+SA. The main topics of the texts cover the contents, such as how to improve creativity, efforts to improve security systems, lack of organized efforts by disaster response organizations, and the role of sports as a means of sustainable development. Although there are some text sections that express subjective opinions, the exam texts mostly include the descriptions of specific methods, problems, and objective information. However, in other related research [17; 18; 19], mainly reviews of products and articles in SNS were used, and the relevant paragraphs revealed a clearer subjectivity and included a relatively large number of expressions of positive and negative affect. In fact, [PERSON] et al. [37] collected review text from an online shopping mall as experimental data and then analyzed it based on the polarity information of the vocabulary, indicating the characteristics of each product. The polarity information for the product is expressed as \"uncomfortable\" or \"ease of use\". The implications of this study are as follows. There are limits to the applicability of SA itself, which can impact the result. SA used in this study is applied at the word level, and the performance of the algorithm itself may be somewhat restricted. For example, after analyzing that the text is composed of three positive words and one negative word, it is judged to be \"positive text\" consisting of two  
Figure 3: Perceived trust of LDA+SA according to affective level. positive words as a whole. In other words, simply counting the number of positive or negative words is not sufficient to interpret the overall meaning of the text. In addition, there are some parts of the collected text data that are not related to the affect. Thus, it is possible to propose a process extracting only portions to be subjected to SA after data collection is completed. For example, statements that only address facts, such as \"buying a new laptop today\", can be categorized as objective texts. These statements could be initially excluded from the analysis. Consequently, the major contribution of this study is to lay a foundation for a transition of prevailing technical viewpoints in the integration of LDA and SA to a user viewpoint. Indeed, there are various studies addressed the integration of LDA and SA through an improved semantic algorithm for LDA [38; 39; 40].