In this paper, we bridge computational linguistics with historical methods to explore the potential of topic modeling in historical newspapers. Our case study focuses on British and American newspapers published in the second half of the 20th century that debate issues of Greek tourism, but our method can be transposed to any diachronic data. We demonstrate that Non-negative Matrix Factorization (NFM) can generate interpretable topics within the historical period under examination providing a tangible example of how computational text analysis can assist historical research. The contribution of our work is two-fold; first, the extracted topics are evaluated both by a computational linguist and by a historian highlighting the crucial role of domain experts when interpreting topic modeling outputs. Second, the extracted topics are contextualized within the historical and political environment in which they appear, providing interesting insights about the historical representations of Greek tourism over the years, and about the development and the hallmarks of American and British tourism in Greece across different historical periods (from 1945 to 1989). The comparative analysis between the American and the British press reveals interesting insights including similar responses to specific events as well as notable differences between British and American tourism to Greece during the historical periods under examination. Overall, the results of our analysis can provide valuable information for academics and researchers in the field of (Digital) Humanities and Social Sciences, as well as for stakeholders in the tourism industry.