Telkom University Opinion Topic Modeling on Twitter Using Latent Dirichlet Allocation During Covid-19 Pandemic
DOI:
https://doi.org/10.30865/mib.v6i4.4426Keywords:
Topic Modelling, Latent Dirichlet Allocation (LDA), Topic coherence, Public opinion, Term Frequency — Inverse Document Frequency (TF-IDF)Abstract
In the current digital era, the development of information technology is growing rapidly. The development of information technology is followed by the development of social media, one of the social media that is on the rise is Twitter. Because there are many Twitter users around the world, Twitter stores a lot of data that can be used for something, one of which is to determine the category of public opinion about a company or university, in this study the focus is more on the category of public opinion about Telkom University. The public opinion can be grouped or categorized to make it easier to determine the topic being discussed. Determining opinions manually will take a long time due to the large number of tweets. Therefore, there must be another method to determine the categories of public opinion on Twitter. One of them is the Latent Dirichlet Allocation (LDA) method with a dataset of tweets of Indonesian-language Twitter users. With this method, grouping tweets on a large scale is more efficient. From the modeling made, the most optimum results obtained with a coherence score using the c_umass method of -15.33029 with a combination of 9 topics, 0.31 alpha value, and 0.01 beta value.
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