Driven by the fierce competition in the airline industry, carriers strive to increase their customer satisfaction by understanding their expectations and tailoring their service offerings. Due to the explosive growth of social media usage, airlines have the opportunity to capitalize on the abundantly available online customer reviews (OCR) to extract key insights about their services and competitors. However, the analysis of such unstructured textual data is complex and time-consuming. This research aims to automatically and efficiently extract airline-specific intelligence (i.e., passenger-perceived strengths and weaknesses) from OCR. Topic modeling algorithms are employed to discover the prominent service quality aspects discussed in the OCR. Likewise, sentiment analysis methods and collocation analysis are used to classify review sentence sentiment and ascertain the major reasons for passenger satisfaction/dissatisfaction, respectively. Subsequently, an ensemble-assisted topic model (EA-TM) and sentiment analyzer (E-SA) is proposed to classify each review sentence to the most representative aspect and sentiment. A case study involving 398,571 airline review sentences of a US-based target carrier and four of its competitors is used to validate the proposed framework. The proposed EA-TM and E-SA achieved 17-23\% and 9-20\% higher classification accuracy over individual benchmark models, respectively. The results reveal 11 different aspects of airline service quality from the OCR, airline-specific sentiment summary towards each aspect, and root causes for passenger satisfaction/dissatisfaction for each identified topic. Finally, several theoretical and managerial implications for improving airline services are derived based on the results.