Data Analytics in Energy Systems: Applications of Keyword Extraction, Topic Modelling, and Text Classification in Text Mining

This study addresses the use of text mining and machine learning methods in the assessment of energy resources. The main objective is to reveal trends, themes and development areas associated with different types of renewable energy by analyzing large volumes of academic data in the literature. In this context, methods such as LDA (Latent Dirichlet Allocation), TF-IDF (Term Frequency-Inverse Document Frequency) and Naive Bayes were used to reveal the contextual structure of the texts and to examine how energy types are associated with each other. The dataset consists of the titles, keywords and abstracts of 25150 open access academic publications taken from the Web of Science database. These texts were vectorized by preprocessing (stop word removal, lemmatization, etc.) and then analyzed with various classification and topic modeling algorithms. In particular, TF-IDF method was used to identify prominent words, while LDA was used to identify possible thematic topics for each energy type. Naive Bayes was considered as a basic model for the classification of energy types. The results show that energy types such as solar, wind and biomass have a dominant place in the literature, whereas types such as wave, tidal and geothermal are underrepresented. In addition, text mining applications can identify not only existing trends but also areas where there are still research gaps. This study proposes a systematic data analytics approach that can guide future research in renewable energy systems. It also provides important contributions in terms of making the information density in the literature more meaningful for policy makers, academics and investors.