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

Light verb constructions (LVC) are an interesting phenomenon that occurs in many languages. It is a category of verbal Multiword Expressions (MWE) and has the canonical form of verb+noun (Constant et al., 2017; Cordeiro and Candito, 2019; Nagy T., Rácz and Vincze, 2020). Examples of LVCs include give help, make a decision, and take a walk. Identifying LVCs is essential for many natural language processing (NLP) applications which include summarization, machine translation, semantic parsing, question answering, and information extraction. The importance of LVC identification to these downstream applications has recently spurred a growing volume of work in both the field of linguistics as well as computational linguistics in various languages as it can potentially increase the performance of these tasks. This paper presents a review of existing work related to LVC identification by summarizing gaps identified and proposing some future work that could bring novel contributions. Keywords: Light verb constructions, Multiword expressions, Natural Language Processing

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The aim of this paper is to highlight two important issues related to the annotation and querying of Intangible Cultural Heritage video datasets. First, we focus on ontology completion by annotating dance videos. In order to build video training sets and to enrich the proposed ontology, manual video annotation is performed based on background knowledge formalized in an ontology, representing a semantics of a traditional dance. The paper provides a case study on Malaysian Zapin dances. Second, we address the question of how can end-users efficiently query the datasets of annotated videos that are built.

Sentiment analysis, also known as opinion mining, is the process of analysing a body of text to determine the sentiment expressed by it. In this study, Natural Language Processing techniques and Machine Learning algorithms have been applied to create multiple sentiment analysis models customized for the gaming domain to determine the sentiment of game reviews. The dataset was collected from Steam and Metacritic through the use of web API and web scraping. This was followed by text preprocessing, data labelling, feature extraction and finally model training. In the training phase, the effects of oversampling and hyperparameter tuning on the performance of the models have been evaluated. Through comparison between Support Vector Classifier (SVC), Multi-layer Perceptron Classifier (MLP), Extreme Gradient Boosting Classifier (XGB), Logistic Regression (LR) and Multinomial Naïve Bayes (MNB), it was evident that SVC had the most superior performance.