## 1 Introduction  
The amount of data processed in technical and social systems has exponentially increased with the advent of the fourth industrial revolution and the era of knowledge information processing. Massive text information and public opinions commonly recorded and shared through various social media services have led to the necessity of new technologies and methodologies to find meaningful information hidden in a large set of available unstructured text data. As a response, text mining has attained attention as a technique for extracting meaningful information from unstructured or semi-structured text, such as documents, emails, and hypertext markup language (HTML).  
Especially, topic modeling is one of the popular text mining methods that enable us to extract highly interpretable topics in a document set. The latent dirichlet allocation (LDA) algorithm is a representative topic modeling approach [1] where a set of documents is grouped into latent topics with a distinct Dirichlet distribution and each topic is described as a Dirichlet distribution of occurring terms in the document set. The LDA algorithm has been applied to various domains, such as topic extraction for the abstracts of a research paper set [2], analysis of news articles to interpret relevant social situations [3; 4], and identification of consumer characteristics and market trends from social network service (SNS) data [5]. However, the traditional LDA algorithm uses the frequency of terms in text documents as a basis to extract their latent topics.  
On the other hand, studies have also developed alternative models on the basis of existing topic modeling algorithms. Sentiment analysis (SA), which is referred to as opinion mining, has been used to identify user attitude, affect, subjectivity, or emotion in a user-generated text [6]. SA distinguishes the affective state, which indicates the positive or negative mood/sentiment of a word or sentence. Thus, SA can be used to collect and analyze vast amounts of data in real-time. Moreover, it can improve perceived trust (the level of trust determined by users based on the extracted information) and minimize errors due to time differences in the investigation process.  
As mentioned above, since text mining algorithms such as LDA and SA are commercialized, information summary analysis and services are being provided in various fields. However, the usability evaluation of the results obtained through the algorithm is not yet actively discussed. The main purpose of this study is to investigate what evaluation criteria are more reliable or satisfactory for users who use both LDA and SA algorithms. Therefore, this study considers a perceived trust of the algorithms from users to reflect the interpretability of the algorithm (i.e., how well the user understands the results of the algorithm). In other words, it is considering not only the frequency of each term but also the affective characteristics of the term in extracting topics from text documents. In addition, this study evaluates the perceived trust characteristics of SA which was applied to the keywords extracted by LDA, and LDA which forms the main application algorithm for topic modeling.  
In order to achieve the objective of this study, the subjective or affective level was defined considering the degree of positivity and negativity in the designated text. In particular, the Stanford natural language processing algorithm, based on machine learning, was utilized to calculate the positive or negative level for each sentence automatically. Through this research process, the user can grasp the change of perceived trust on the result of existing topic modeling algorithms such as LDA and SA. This study also shows the necessity of a topic modeling algorithm that can reflect the affective level of each topic.