AN ANALYSIS OF PUBLIC PERCEPTION TOWARDS TECHNICAL AND VOCATIONAL EDUCATION AND TRAINING (TVET) ON THE FACEBOOK PLATFORM UTILISING LEXICON-BASED APPROACH

Abstract

The Malaysia government consistently emphasises the importance of Technical and Vocational Education and Training (TVET) as a crucial asset for the country, pushing all parties involved to prioritise it. Due to the rapid increase in social media usage, the public is becoming more likely to use social media platforms to interact and discuss various challenges in this field. An abundance of ideas can be accessed on the internet and should be employed to comprehend the viewpoint of important individuals involved and make necessary alignment to strategies and services.  Unlike fields such as healthcare, business, and tourism, numerous sentiment studies have been conducted to examine the public perception with the goal of improving both services and products. However, there has been little investigation conducted on TVET. Therefore, this study is conducted to fill the gap by extracting TVET data from Facebook pages and public groups. The sentiment applied term frequency-inverse document frequency (TF-IDF) vectorization for extraction of valuable information evaluated by employing the accessible lexicons, namely Sentiwordnet, Valence Aware Dictionary sEntiment Reasoner (VADER), TextBlob, and AFINN. The evaluation results revealed that all lexicons exhibit a positive sentiment towards TVET. Moreover, by knowing the sentiment, it can facilitate policy makers and decision makers in formulating policies and strategies, as well as tackling existing difficulties and challenges for an enhanced TVET environment in the future.