Twitter is one of the most prominent social networking platforms so far. Millions of users utilize Twitter to share their thoughts and views on various topics of interest every day resulting a huge amount of data. This data could be considered to have a rich source of useful information hidden inside. Using machine learning to this data may give rise to effective recommender frameworks for individuals to manage their lives in a much more convenient way. In this paper, we propose a machine learning approach to classify the passenger’s tweets regarding the airplane services to understand the pattern of emotions. We adopt Random Forest (RF) and Logistic Regression (LR) to classify each tweet into positive, negative and neutral sentiment. The evaluation of the collected real data demonstrates that these two methods are able to achieve an accuracy ≈80%.

Kumar S and M Nezhurina (2018). An ensemble classification approach for prediction of user’s next location based on Twitter data. Journal of Ambient Intelligence and Humanized Computing. https://doi.org/10.1007/s12652-018-1134-3.

Yee L and P Tan (2014). Gaining customer knowledge in low cost airlines through text mining. Industrial Management & Data Systems, 114(9), pp. 1344-1359.

Twitter: www.twitter.com access on 11.02.2019

Zhang L, Y Sun and T Luo (2016). A framework for evaluating customer satisfaction. In Proc: International Conference on Software, Knowledge, Information Management and Applications (SKIMA), IEEE, Chengdu, China, 15-17 December.

https://doi.org/10.1007/s11263-007-0056-x.

Tiwari P and M Melucci (2019) Towards a Quantum Inspired Binary Classifier. IEEE Access, 7:42354-42372.

Kusen E and M Strembech (2017). An analysis of tweeter discussion on the 2016 Austrian presidential election, arXiv preprint arXiv: 1707.09939.

Ahmed S, K Jaidka and J Cho (2016). The 2014 Indian elections on Twitter: a comparison of campaign strategies of political parties. Telematics and Informatics, 33 (4), pp. 1071-1087.

Stieglitz S and L Dang-Xuan (2013). Emotions and information diffusion in social media - sentiment of microblogs and sharing behavior. Journal of management information systems, 29 (4), pp. 217-248.

Gunarathne P, H Rui and A Seidmann (2017). Whose and what social media complaints have happier resolutions? Evidence from Twitter. Journal of Management Information Systems 34 (2), pp. 314-340.

Seunghyun BP, C Ok, B Chae (2016). Using Twitter Data for Cruise Tourism Marketing and Research. Journal of Travel & Tourism Marketing, 33 (6), pp. 885-898.

Gräbner D, M Zanker, G Fliedl and M Fuchs (2012). Classification of customer reviews based on sentiment analysis. In: Fuchs M, Ricci F, Cantoni L (eds) Information and Communication Technologies in Tourism 2012, Springer, Vienna.

Data-set:https://data.world/crowdflower/airline-twitter-sentiment accessed on 12.01.2019.