Although measuring the ideal points of news media is essential for testing political communication theories based on spatial theory, prior methods of estimating ideal points of media outlets have various shortcomings, including high cost in terms of time and human resources and low applicability to different countries. We propose that unsupervised machine learning techniques for text data, specifically the combination of a text scaling method and latent topic modeling, can be applied to estimate ideal points of media outlets. We applied our proposed methods to editorial texts of ten national and regional newspapers in Japan, where prior approaches are not applicable because newspapers have never officially endorsed particular parties or candidates, and because high-quality training data for supervised learning are not available. Our two studies, one of which analyzed editorials on a single typically ideological topic while the other investigated all editorials published by the target papers in one year, confirmed the popular view of Japanese newspapers’ ideological slant, which validates the effectiveness of our proposed approach. We also illustrate that our methods allow scholars to investigate which issues are closely related to the respective ideological positions of media outlets. Furthermore, we use the estimated ideal points of newspapers to show that Japanese people partially tend to read ideologically like-minded newspapers and follow such newspapers’ Twitter accounts even though their slant is not explicit.