Machine Learning in Action: Topic-Centric Sentiment Analysis and Its Applications

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].