Abstract

This article discusses topic-level sentiment analysis using machine learning techniques such as topic modeling and Latent Dirichlet allocation (LDA). Topic modeling is an unsupervised machine learning method that clusters words in a document set without the need for pre-defined training data. Although quick and easy to start with, it may not always yield accurate results. In contrast, supervised machine learning techniques like topic classification models require training and manual labeling for better accuracy, providing more valuable insights for data-driven decision-making. LDA, a popular topic modeling technique, assumes that similar topics use similar words and documents discuss multiple topics. It maps documents to a set of topics based on word distributions and ignores grammatical information, treating documents as bags of words. LDA uses hyperparameters alpha and beta to control the similarity between documents and topics. The number of topics must be set manually, and recent research has focused on optimizing these hyperparameters. The article also includes a table showing the probability of words belonging to different topics as identified by LDA [1, 2, 3, 4].