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Analysis Of Us Airline Stocks Performance Using Latent Dirichlet Allocation (Lda), Amina Issoufou Anaroua
Analysis Of Us Airline Stocks Performance Using Latent Dirichlet Allocation (Lda), Amina Issoufou Anaroua
Beyond: Undergraduate Research Journal
Various events, such as changes in the interest rate or the hijacking of a commercial aircraft, can lead to significant shifts in airline stock performance. This study aimed to measure the impact of aviation-related news announcements on the stock performance of US airlines, focusing on different topics. The dataset included aviation news covering airlines, airports, regulations, safety, accidents, manufacturers, MRO, incidents, aviation training, general aviation, and others obtained from Aviation Voice. To uncover patterns that could explain the movements of US airline stocks, a natural language processing technique called Latent Dirichlet Allocation (LDA) was employed. The process involved text mining …
Business Inferences And Risk Modeling With Machine Learning; The Case Of Aviation Incidents, Burak Cankaya, Kazim Topuz, Aaron M. Glassman
Business Inferences And Risk Modeling With Machine Learning; The Case Of Aviation Incidents, Burak Cankaya, Kazim Topuz, Aaron M. Glassman
Publications
Machine learning becomes truly valuable only when decision-makers begin to depend on it to optimize decisions. Instilling trust in machine learning is critical for businesses in their efforts to interpret and get insights into data, and to make their analytical choices accessible and subject to accountability. In the field of aviation, the innovative application of machine learning and analytics can facilitate an understanding of the risk of accidents and other incidents. These occur infrequently, generally in an irregular, unpredictable manner, and cause significant disruptions, and hence, they are classified as "high-impact, low-probability" (HILP) events. Aviation incident reports are inspected by …