In recent years, communication via internet has rapidly increased, particularly through social media sites. This was also the case across the Maltese Islands, with a marked increase in popularity of social media websites, especially Facebook.
As with other populations, the Maltese tend to make extensive use of this platform to make their voice heard, posting hundreds of reviews regarding various subjects. The purpose of this work was to propose a sentiment analyser for such comments/posts, using novel methodology. Although there are quite a number of similar projects for the English language, this was not the case for the Maltese language, due to the challenges it presents, mainly due to being considered a low-resource language.
Sentiment analysis is the process of analysing data and categorising it as positive, negative, or neutral, depending on the overall sentiment of the given text. At the beginning of the project, a new dataset was specifically built for the task at hand. This consisted of a number of
comments extracted from popular Maltese-language Facebook pages, written by the Maltese public, and mainly in Maltese. Human annotators were tasked with correcting the said comments in terms of grammar and spelling. The comments were then classified into the aforementioned sentiment categories. This exercise yielded a labelled dataset, on which the proposed sentiment analyser would function.
The proposed sentiment analyser is capable of performing preprocessing on the given data, cleaning the text of any redundant characters and preparing the data for feature extraction. The
sentiment analyser is able to extract hand-crafted features, thus allowing data classification at a context-window level. In this way, one could easily obtain virtually within seconds the sentiment of any comment written in the Maltese language.
The developed sentiment analyser has an accuracy of around 84% on the corpus at hand, thus offering a fairly reliable data sentiment analysis for the Maltese language.
Student: Christopher Galea
Course: B.Sc. IT (Hons.) Artificial Intelligence
Supervisor: Prof Alexiei Dingli