The primary objective is to measure the impact of US airlines’ stock performance following aviation related news announcements of differing sentiment and/or topics. The categories which account our data for aviation news are fuel prices, interest rate, inflation, airfares, foreign exchange rates, capital expenditures, and growth in output. The amount of such data and documents is expected to be enormous. So, we use a natural language processing, Latent Dirichlet Allocation (LDA) to investigate and search for patterns that can explain the movement of US airline stock. First, we will mine the aviation related data through text mining and topic modeling. Second, we will employ the LDA model approach to help us identify and capture the extent of certain topics mentioned in aviation news releases. Finally, we will use Event Study Methodology (ESM), which is a common method used to investigate stock price reactions to news announcements. We will apply this method to discover the significance of the relationship between the stock return and the associated event. Findings of this research will help stakeholders or investors understand the relationship between news of the aviation related events and the stock returns of the US airlines.