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Recently, as the value of text data increases, various studies on text analysis have been required, especially in the area of failure diagnosis. However, there are almost no results that systematically address the text analysis problem in the literature. Motivated by this concern, this paper focuses on developing a text analysis method that can be applied to failure diagnosis via the well-known text mining and deep learning. The main objective of this study is to analyze the failure diagnosis data of electric equipments by building an algorithm that can predict the departments (or companies) that provides the cause of the failure. To be specific, the proposed algorithm offers a possibility 1) to shorten the maintenance time, 2) to minimize labor costs, and 3) provide clear criteria of selecting subcontractor through statics and visualization. Methodologically, the data preprocessing such as feature extraction and tokenization enables the raw text data to be analyzed. After that, on the basis of machine learning and deep learning models (including CNN and RNN), various experiments are conducted to select some suitable models with high performances. Furthermore, this study makes an attempt to optimize the selected models by means of parameter tuning and dropout techniques. Finally, using the most recent data, the effectiveness of the proposed algorithm is verified.