Avianca sentiment analysis
DOI:
https://doi.org/10.26507/paper.4548Keywords:
sentiment analysis, customer perception, natural language (NLP), machine learning (ML), data analysisAbstract
The airline industry faces constant challenges due to high competition, fluctuations in demand, and the need for high service standards in a regulated environment. In this context, customer perception influences the reputation and sustainability of airlines, affecting passenger loyalty and market differentiation. With the increasing use of social media and review platforms, these have become a valuable source of information on customer experience.
This study focused on sentiment analysis applied to passenger comments about Avianca on social media. Its objective was to assess user perception and provide relevant insights for the airline's strategic and operational management. To achieve this, comments from platforms such as Instagram and TikTok were collected and classified into two categories: positive or negative.
In the analysis phase, opinion classification methods and natural language processing (NLP) techniques were employed to structure the information. These approaches optimized the analysis of large volumes of data, facilitating the identification of satisfaction and dissatisfaction patterns.
The results indicate that comfort, service efficiency, and customer care generate positive perceptions, while delays, baggage handling, and response to unforeseen events lead to negative comments. This analysis provides Avianca with a key tool to identify areas for improvement and optimize loyalty strategies. From an organizational perspective, it offers a data-driven strategic approach, allowing for resource optimization, staff training enhancement, and the design of more effective service policies. This, in turn, strengthens customer relationships and competitiveness in the airline market.
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