Online assessment systems are increasingly utilised as an evaluation tool for measuring the performance of lecturers in Institutions of Higher Learning (IHLs). These systems commonly have a set of questionnaires comprised of quantitative and qualitative questions. Most online lecturer teaching assessment systems are focused on the quantitative part of the questionnaire since it is easy to analyse. On the contrary, the qualitative part, which requires students' opinions, is often omitted and neglected, and the level of opinion results are excluded. This is because students' opinions are usually in the form of unstructured texts, which makes it hard to manually analyse all the feedback. To address this problem, a system that could automatically analyse students' feedback (known as OMFeedback) was developed. This system applies an opinion mining technique to reveal the teaching assessment results, which are underpinned by a lexicon-based approach. Lexicon-based is a common textual data quantification method that can analyse the sentiment tendency of a student's feedback. A database of English sentiment words, known as the Vader Lexicon, was utilised as a lexical source to determine the polarity of words. By analysing these sentiment words, which included intensifier words extracted from students' feedback, this system was able to determine the results for the lecturer teaching assessment by describing the level of positive, negative, or neutral opinions. This system was also able to process new features, such as capitalised words and emoji characters to enhance the opinion results. Ultimately, this newly developed system will provide useful information to IHLs for improving lecturers' teaching skills and course materials.