Analysis and Visualization of Tweets Using Data Science: A Case Study of Qatar Airways

Data mining has gained significant importance in various fields due to its ability to extract information from large datasets. This is especially true in a period where social media platforms and review websites provide an open arena where airline passengers can express their opinions, airlines deal with large amount of unstructured data which contains important information about the feelings and preferences of customers. With the rising popularity of data science and interdisciplinary research, this paper aims to understand, examine and interpret the main concerns and emotions of people regarding the world’s most top-rated airline, Qatar Airways by implementing sentiment analysis and topic modelling on Twitter data for extracting public sentiments and topics of discussion. We created a data set comprising of tweets for Qatar Airways. The approach starts with crawling tweets for a three-week time period, from 01 June 2023 to 21 June 2023, followed by pre-processing and lastly, implementing sentiment analysis and topic modeling. The analysis was carried out using RapidMiner and Orange. After removal of irrelevant tweets and applying sentiment analysis on a data set of 1305 tweets, 626 tweets were categorized into positive, 195 as negative and 484 as neutral sentiments. After analysis, public sentiment is found to be predominantly positive. Positive reviews were focused on good experience in business class, negative reviews were mostly concerned with baggage handling and neutral opinions were towards sponsorship by the airline. The study has combined knowledge extraction with sentiment analysis. Visualizations of sentiment count and topic modeling have also been presented.