Sentiment analysis has grown to be one of the most active research areas in Natural Language Processing (NLP). Sentiment analysis, also known as opinion mining, uses a series of methods, techniques and tools to study people’s opinions, views and sentiment towards a wide range of topics such as products, services, events and issues. In the airline industry, millions of people today use social networking sites such Twitter, Skytrax, TripAdvisor to express their emotions, opinions, and share information about the aircraft service. It is a hidden gem to the airline company to gain valuable insight from this data and have the broadest possible view into what people are saying about the airline’ brand online. Hence, this paper explores six different sentiment analysis models: Random Forest, Multinomial Naive Bayes, Linear Support Vector Classifier, Ensemble Method, Bidirectional Long Term Short Memory (Bi-LSTM) and BERT model, in order to determine and develop the best model to be used. The best model was then used to determine the social status, company reputation, and brand image of Malaysian airline companies. In conclusion, the BERT model was found to perform the best out of the six models tested, scoring an accuracy of 86%. Keywords: Supervised Learning, Ensemble Learning, Deep Learning, Transfer Learning, Airline Sentiment

Computer vision is one of the basic features to streamline processes like robotic process automation and digital asset management. Computer vision has come a long way in terms of its capabilities and what it can provide and do for different industries. Object detection and image detection are just some of the few applications provided by computer vision. However, this field is still relatively young and prone to challenges. The first is the lack of wellannotated images to train the algorithms to perform optimally, and the second being lack of accuracy when applied to real-world images different from the ones from the training dataset. As such, this paper aims to fine-tune pre-trained machine learning models, which are ResNet50 and VGG19 as well as training a new SqueezeNet inspired model from scratch to create a flower recognition model that can process and remember large amounts of flower species data. In conclusion, VGG19 was found to perform the best on both the 5 Categories and Flower-102 dataset, with an accuracy of 88% and 84% respectively. Keywords: VGG19, Transfer Learning, Deep Learning, Flower Recognition, Neural Network

Sentiment analysis has been a popular research area in Natural Language Processing (NLP), where sentiments expressed through text data including positive, negative and neutral sentiments are analyzed and predicted. It is often performed to evaluate customer satisfaction and understand customer needs for businesses. In the airline industry, millions of people today use social networking sites such Twitter, Skytrax, TripAdvisor and more to express their emotions, opinions, reviews and share information about the aircraft service. This creates a treasure trove of information for the airline company, showcasing different points of views about the airline’s brand online and providing insightful information. Hence, this paper experiments with six different sentiment analysis models in order to determine and develop the best model to be used. The model with the best performance was then used to determine the social status, company reputation, and brand image of Malaysian airline companies. In conclusion, the BERT model was found to have the best performance out of the six models tested, scoring an accuracy of 86 percent.